{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# BankRenegeSimulator 项目展示\n",
    "\n",
    "基于 SimPy 2 教程的 `TheBank` 教程的程序 `bank08.py` 改进。\n",
    "\n",
    "项目展示了 SimPy 仿真工具包良好的可封装特性，**对于更加宽泛且常用的仿真系统，建立 SimPy 模型后可以进行二次封装和调用。** 以实现资源的统筹、规划、管理和针对性应用。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "涵盖的内容：\n",
    "\n",
    "- 多服务台并联\n",
    "- 排队系统\n",
    "- 条件事件\n",
    "\n",
    "场景：\n",
    "\n",
    "一组服务时间随机的柜台，可能顾客因等待时间过长而离开。\n",
    "\n",
    "经过改进和封装后后的代码包含两个类：\n",
    "\n",
    "- `BankSimulator` 银行模拟器类\n",
    "- `queue_info` 仿真结果信息类\n",
    "\n",
    "排队信息保存为 `DataFrame`，传递给 `queue_info` 的 `__init__`，把输出结果变成一个“输出结果类”，对这个类进行实例化之后，只要对这个类的属性和方法进行调用，就可以实现仿真结果的查看和绘图。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 导入工具包"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import random\n",
    "import simpy\n",
    "import pyforest\n",
    "from BankRenegeSimulator import *\n",
    "pd.set_option('display.max_rows', 10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 设置仿真参数"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "RANDOM_SEED = 42           # 随机种子\n",
    "NEW_CUSTOMERS = 600        # 顾客总人数\n",
    "INTERVAL_CUSTOMERS = 10.0  # 大约每 n 分钟生成一名顾客\n",
    "MIN_PATIENCE = 1           # 顾客的最小耐心时间\n",
    "MAX_PATIENCE = 10          # 顾客的最大耐心时间\n",
    "NUM_COUNTER = 1            # 银行柜台数\n",
    "TIME_IN_BANK = 10.0        # 顾客滞留在银行里的时间\n",
    "VIP_SCALE = 0.1            # 顾客中 VIP 的比例"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 实例化仿真环境和仿真器"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "random.seed(RANDOM_SEED)\n",
    "env = simpy.Environment()\n",
    "simBank = BankSimulator(\n",
    "    env, \n",
    "    NEW_CUSTOMERS,\n",
    "    NUM_COUNTER,\n",
    "    INTERVAL_CUSTOMERS,\n",
    "    TIME_IN_BANK,\n",
    "    MAX_PATIENCE,\n",
    "    MIN_PATIENCE)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 运行仿真环境"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "%%capture\n",
    "env.run()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "由于输出结果太长，输出结果被隐藏了。下面截取部分运行结果进行展示：\n",
    "\n",
    "```\n",
    "...\n",
    "7.5983 25: Here I am\n",
    "37.7295 26: Here I am\n",
    "38.9919 27: Here I am\n",
    "39.2378 20: Finished\n",
    "39.2378 24: Waited  1.971\n",
    "40.2984 19: Finished\n",
    "40.2984 25: Waited  2.700\n",
    "40.4544 24: Finished\n",
    "40.4544 26: Waited  2.725\n",
    "42.8030 23: Finished\n",
    "42.8030 27: Waited  3.811\n",
    "42.9353 28: Here I am\n",
    "43.7669 29: Here I am\n",
    "44.9762 30: Here I am\n",
    "47.8468 31: Here I am\n",
    "47.8482 28: RENEGED after  4.913\n",
    "51.9352 32: Here I am\n",
    "53.3524 33: Here I am\n",
    "53.5139 29: RENEGED after  9.747\n",
    "54.1898 30: RENEGED after  9.214\n",
    "54.2292 11: Finished\n",
    "54.2292 33: Waited  0.877\n",
    "54.3108 32: RENEGED after  2.376\n",
    "54.3526 26: Finished\n",
    "...\n",
    "```"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 利用 .result() 接口快速提取仿真结果"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "result = simBank.result()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "`result` 实例内置了可直接调用的绘图方法 "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(<Figure size 2400x1200 with 2 Axes>,\n",
       " array([<Axes: title={'center': 'wait time'}>,\n",
       "        <Axes: title={'center': 'queue length'}>], dtype=object))"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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AK4ViLaCQ5DaI9cWHk1Sy1vqmlXMAAAAAMO6yiq5mphoxI1kLAACgI8VaQCHNVufJlKl63mQtkzEAAAAAMO6yiq5mVtWTizlTWycCAACsFIq1gEJSyVr12uJirZmpzjHnkrUAAAAAYPxlFV1NN+oZizltgwgAAKxsirWAQlqJYq1lyVpTVs4BAAAAwKTqNVnLYk4AAGClU6wFFJJK1mo0liZrJSZjFGsBAAAAwNjrlqyVSt6fnWtFu915jhEAAGAlUKwFFNLsO1lLzDkAAAAAjLuseb6ZVY3k/GC7nV4QCgAAsBIo1gJya7fb6WSt2tJkrfTKOQAAAABgvGUl6D+UrJW+/GCOEAAAWMkUawG5ZS14ayxJ1ppZlUrWMhEDAAAAAOMuNc9Xq0WsatSSyVoR2YVeAAAAk06xFpBbagvEiIipxpJtEBuJYq0NJmIAAAAAYNylCq6mG/Wo1WrJ5P2I7C0UAQAAJp1iLSC3rGKtRn3x4SSVrLW+qVgLAAAAAMZdquBq4/aHkrUAAAA6U6wF5DbXSk+iTC3dBjGZrGXVHAAAAACMu9Q2iDOrHkrUmsko1ko9FgAAYCVQrAXklpWsVa8tKdZa1TnmXLIWAAAAAIy/VMHV9K8WcWYWa20wRwgAAKxcirWA3OYyirWmGouLtaaTyVomYgAAAABg3KWTtXJsg9iUvg8AAKxcirWA3FoZxVqNpdsgrup8eJGsBQAAAADjb32XZK3UYs4ICzoBAICVTbEWkFtmstbSYq3EyrnZOavmAAAAAGDcpeb5ZlY1IiKiVqsl07VmLegEAABWMMVaQG7NAslayYkYq+YAAAAAYOylkrVmFiRqJRd0miMEAABWMMVaQG5ZyVrLtkGcanS8n20QAQAAAGD8zaaKtVZ1L9YyRwgAAKxkirWA3AolazWsmgMAAACASZVM1ppaWKzVeUHn7IbOWygCAACsBIq1gNyyirWm6osPJwtX0C1k1RwAAAAAjL/Zuc4FV9MLirWmJWsBAAAso1gLyG2ulZ5EyZus1Wy1Y85kDAAAAACMtXSyVmPBv6XvAwAALKVYC8ityDaIM6s6R5xHWDkHAAAAAONuNlGstXARZ7JYK/FYAACAlUCxFpDbXOY2iPmStSKsnAMAAACAcZea45tZlWMbRMVaAADACqZYC8itVShZK6NYy2QMAAAAAIy1VHr+4mStzun7s3PNgbQJAABgHCjWAnLLStZq1JYUayVWzUVYOQcAAAAA4y5VcCVZCwAAIJtiLSC3ZqJYq16LqC/dBjGjWMvKOQAAAAAYb6mCq+nGw2laqQWdkvcBAICVTLEWkFsqWWvpFogR6YjzCJMxAAAAADDuUnN8krUAAACyKdYCcmu2Ok+idC7WykrWMhkDAAAAAOMsVXC1cF4wnawleR8AAFi5FGsBuTUTNVZT9eWHkulG+vBi5RwAAAAAjLfUgszpRcVandP3LeYEAABWMsVaQG5FkrXq9Vqsaiz/eoSVcwAAAAAw7mY3dJ7jW1igZRtEAACA5RRrAbnNtdodvz7VoVgrwso5AAAAAJhU6xMx/NO5tkE0PwgAAKxcirWA3JqJYq16oljLyjkAAAAAmEyzGzrP8S0s0DI/CAAAsJxirRK02+148MEHo9m0tRuTLVWslU7WsnIOAAAAACbRbK5krVTyvrl0AABg5VKs1YO5ubn4zGc+E895znNihx12iKmpqdhss81iZmYmHvOYx8Q73/nOuO2220bdTChdahvERsFiLSvnAAAAAGB8tdvt5BxfnmQtizkBAICVTLFWQdddd10cfPDB8apXvSrOPPPMuOOOO6LVeujEstlsxtVXXx1/+7d/G3vvvXf8+7//+4hbC+UqmqyVnoyxcg4AAAAAxtX6RKpWxOI0LYs5AQAAlpsadQPGyQ033BBHHHFE3HzzzV3ve//998cxxxwT7XY7Xv7ylw+hdTB4qWStejJZq3PMuckYAAAAABhfWclYM4u2QZSsBQAAsJRkrZxarVYcc8wxiwq19ttvv/j4xz8e1113XaxZsybOPvvsePazn73oMW9+85vjzjvvHEWToXTNxIq54slaJmMAAAAAYFzNbshXrGV+EAAAYDnFWjn967/+a5x77rnzt9/+9rfHZZddFq973etizz33jK233joOO+ywOOOMM+L444+fv99dd90VJ5100iiaDKVrdg7Wika986EkvXLONogAAAAAMK6ytkGcXpSs1Tl53/wgAACwkinWymFubi7e+973zt/+gz/4g/jABz4Qq1atWnbfWq0W//RP/xTbb7/9/Ne+8pWvDKWdMGjNVjnJWrZBBAAAAIDxNbshXWy1sEArtZjT/CAAALCSKdbK4Wtf+1rceOONERGx2267xfvf//7M+2+66abx3Oc+d/72VVddNdD2wbDMtTpHazUSxVrpZC2TMQAAAAAwrvIna6XnB9vtRIw/AADAhJsadQPGwczMTBx33HFxxRVXxKte9arYdNNNuz5m5513nv/3hg0bYm5uLqamvNyMt2ZiH8R0sVbnmHMr5wAAAABgfM1uSM/vLSzQSiXvR0RsaLZjeqrzvCIAAMAkUz2Uw7Of/ex49rOfXegxt99++/y/N910U4VaTIRmYrVbqlgrNRkjWQsAAAAAxldWstbMomStzos5IyJm55qZxVwAAACTypnQgJxzzjnz/95///1H2BIoTzOxDeJUwW0QJWsBAAAAwPhKJWvVaxFTjQXFWqvSlyDMEQIAACuVYq0BuPTSS+Pqq6+ev/3c5z53hK2B8swlirWKJ2s1S2sTAAAAADBc65ud5/eWJmlNN9KXIKTvAwAAK5VirQH40Ic+NP/vWq0Wxx577AhbA+VJJWulirVSMecmYgAAAABgfKWStZYu3pSsBQAAsNzUqBswaS6//PL43Oc+N3/7RS96Uey7774jbBGUZ65ZbBvEVLLWd6+5M/70y5fHEftuH7+5345Rq3V+PKN39W33xX/94OdxzW33z3+t0ajFr+26ZbzoSbvG9qtnRtg6mBy/XD8XX/r+LXHx9Wti3YIJ71233jSe+/id4smP2maErQOAyfWjW+6J//7hrXH9nQ/kuv/0VD2e/Kit48UH7hKrN1k14NYBsFCr1Y7/+uHP45xr74x7H5wr9bmnGrV44m5bxYsP3DW22Xy6r+e6b92G+NL3b4krfn5PPH6XLePFB+4am89Uexq+3W7H1y7/RXz3mjti7S83zH99q81WxWH7bB+/9fidls3fpRZjziyZD8xK1vrz038UWyx4bXbacpM46nE7xtP32a6XXwOADv7nJ7fF//zk9rjzvvWFH1urRey38yPi+U98ZDxq280H0DoAWLmqfZY4Ztrtdrz5zW+O5q8ioKempuJ973tfac9/++23xx133FHoMddee21pPx9a7aLJWunJmNMuuDFOu+DG+D/PeHT88bMfW0r7KNdF16+JV//rhfHL9ctj7b/6w1vjtAtujH8/4ZDY8RGbjKB1MDl+uX4ujvvXi+LC69d0/P6/nvOzOOl3nxgveOIuQ24ZAEy2/73q9jjh05fE+maxVI+v/ODn8aVLb4lTX/s0BVsAQ9Jut+Nd//HD+MIlNw/sZ/z3D2+Nf7vopvjc7x/c8+K0+9ZtiFd+8sK47Ka1ERHx+YtvjtMv+3l8+jVPq3TB1l/+95Vx8jnXd/zev110Uxx36B7x7ufvv+jrqVSs5clanZP3IyK+d+2dy752yrnXx7uft18c9+t7dmk1AN38/beviQ998+q+nuOMH/0iTj73+vjc7x8cj9lpdUktAwBsg1iij370o3H22WfP3/6DP/iDeNzjHlfa83/kIx+JAw44oNB/L3zhC0v7+TDX6jwJM1XvfCjJKtba6CPf+WmseaD4ig4G7+++dU3HQq2Nrr/rl3HaBTcOsUUwmb555W3JQq2IiFY74m++flW0EwWzAEBvPnDmVYULtTb6wU1r42uX31pyiwBIufq2+wdaqLXRtbffH1+45KaeH3/G5b+YL9Ta6JIb7o5vXPmLPls2OLesfTBZqLXRKedeH7esfXDR12bnOs8ZFUnWSvnbM6+yRSJAn+5dtyH+/tvXlPJcax5YH/981k9LeS4A4CGKtUpy7bXXxjvf+c7523vttVe8973vHWGLoHzNVrFkrdWbdF8x2G5HXJxRpMDoXJTjfclzHyDbxdff3fU+t6x9cNnEOADQuwdm5+KKn9/b13Nc8DNjYYBhGeb8w4V9HN9TC3H6ec5Byzsvt/R+qW0Qp6cWJ2mtatRyLehc6IH1zbjy1v76aYCV7rIb18Zc4ppOL1wLAIByKdYqwfr16+PlL395PPDAAxER0Wg04jOf+Uxsvrn9m5ksc81ixVqH7LVdrud9cEM6vYnRmGu2kpNuC2UlbwH5rMt5DHzQ3xsAlKaMcxB9M8Dw5D1vKkM/cx2/XD9X+nMO2gOz+dq29HdIzRstLcyq1Wpx6KO3Ldyu1GsJQD5lH0fXbZB4CABl6h57Q1f/9//+37jkkkvmb5944olx6KGHlv5z3vSmN8XLXvayQo+59tprbYVIaYoma+2+7WbxB0fuHf/wP9dmPu+sQX7l5N0OJk9BF5AtdWxdyt8bAJSnjH5V3wwwPHnPm8rQz/E9NcdV5bmv1HaGy+63pGAutU1hpxStdz33sfHDm++Jux5YX6Bd1X3NAMZB2cfRZstxGQDKpFirT1/84hfjpJNOmr/9rGc9K/7sz/5sID9rhx12iB122GEgzw15NNudJ8amEsVaERFv/83HxNP33i7OufbO+PtE0VbeSSGGJ+8kovcO+pc6ti5lohoAyrP0gvNCL3vyrrHZ9MNbOF1609r44c33LH8OY2GAoUmdN223xUz89uN36uk5r7z13riow7b0qSKkPFLnbVXuM/Keay69X3obxOXFWo/d6RHx1T88LL7149vimtvuW/S9z114U8dFg1UucAMYB6nj9BYzU/GSA3dJPu7GNb+M/73qjmVfH2bhNACsBIq1+vDjH/84XvOa18zf3nXXXeOzn/1s1Ot2l2QypfY3TyVrbXTQXtvGQXttG9/88e3x41vvXfZ9BQjVkzdZq58JTOAhqWPrUlWe3AeAcZM13v2T33pcbLP59Pztv/vWNR2LtYyFAYan2ex83rTHtpvFe15wQE/P+Znzru9YrNXPuVeqb8g7zzIKefuzpfdLvU4zU42OX99py03i2IMftezrX7381rjz/uWJW1V+zQDGQeq6yw6PmMnsO7915W2KtQBgCFQV9ejuu++O5z//+XHffQ+tBJqZmYn/+I//iO23337ELYPBSU2MZSVrLdQpBj1CsVYV5U/W8t5Bv1LH1qX8vQFAebLGu0vPW2ZWOY8BGLVeFxBmSRUV9ZPolCpgqnJKVO5tEJf0e0W2QcySfh8sWALoR+o4mjrubtRodO5b8y44BQDyUazVg/Xr18dLX/rSuPbah7d0+8hHPhJPe9rTRtgqGLzUYLyec2KsUwx6hBXpVbS+mW9CzHsH/cs70eHvDQDKk5XWsfS8ZbrhPAZg1FJpHv0UayXnqfpIdEoV8lY5JSp3slZzabJWOcVag3gfAEgfR1PH3Y0atc59q2QtACiXYq2CWq1WHH/88fE///M/819785vfvGg7RJhUrbZkrZViXe5kLascoV+pY+tSjpUAUJ5Uwkm9tvz8RrIWwOg1E+dN/SVrJY7vfSQ6pQqfqp2slXMOaMnrkvpduxUBLJV+H6r7mgGMg9RxtFtRbep6T6ovBgB6o1irgHa7Ha9//evjtNNOm//akUceGSeddNIIWwXDk46cz3coSZ0EWJFePXlXL66fa0XbSRr0RbIWAAxfKkl2ZqoRtSUryVPbhOibAYYnleaRdwFhJ6liXMlaifstS9ZK9aXlFGtV+TUDGAep42i343SqELrdjmhJ1wKA0ijWKuDP/uzP4pOf/OT87b333ju+8IUvxNTU1AhbBcPTbHUe3OdP1up8kUM6U/XkXb3YaturHvqVOrYu5VgJAOVJjXc7pYGkEkL0zQDDM9fsbwFhJ9ON1DxV7wvTUsVa/aR1DVr+ZK3F90sVec2s6vy6piT7WclaAH3pOVmrkb7e41oAAJRHlVFO5513Xvz1X//1/O3Vq1fHF77whdhss81i3bp1XR+/atWqaCQmAGBcpCbG6n1ug2hFevUUWb24fq4Vqxpqf6FXqWPrUo6VAFCeIqvMbecOMHr9LiDsJJWs1f7VwrRVGRerU9YnCnmrnBKV91xzdlmyVqLwueAcUTLBMpGCCUA+WWnCWbIKoVNJlwBAca6u5/ShD31o0Yqq++67L570pCfFpptumuu/z3zmMyNsPZSj38j59Ir06k5YrVRFVnx6/6A/eSc5/K0BQHnKSdbSNwMMSyrJI7VVUx5ZRUW9HuPTyVrV7TPyJkUu/R1Sv2vRbRAlawEMRpFznoUataxkLcdmACiLYq2czj777FE3AUaumYiAzzsxll6RbqVc1RSZlPT+QX9Sx9alTFQDQHlSY9giyVrr+9gmC4BiWn3OSXWSStaK6G3bwna7nS7WqnCBb+5tEJf0nalErm5FAEtJsAQYjF6LarP6VrVaAFAexVo53XHHHaNuAozcoJK1bO1VPUXeE+8f9CdvspYtIACgPMmtmzpsCZJ1MaPK21oBTJLU9vH9bIOYlazVy/F9Q8YW9+ub1S3wzVsUtXT+p0jhcxbzhQCD0WtR7VTGNsCStQCgPFOjbsC4aBmAQHJiLH+yVue90K2Uq55iyVreP+hH6ti6lGQtAChPkVXmqfOYjc+T9X0AypFa5NJfslbG8b2H869uyeOzc63YJONnjkr+ZK3F90sXART7HSXxAwxGr0W1WX1r3kWnAEB3krWA3PqdGLNSbnysLzAh5v2D/uRP1vK3BgBlSY1hO124yFp5biwMMBxzgyjWKjk5sVufUNVzurx92fJkrd6211oqOV9Y0dcLYFykjqNdk7Uy+tZUfwwAFKdYC8gtFXGbN3I+vVLO5EvVFEvWstIR+pE3PlyyFgCUJ70NYqdkrfTUiXMZgOEYRLJW1sXq3pK1sh9T1XO6vPM6S++XLHxeVeySQzKJv6KvF8C4SB1HuyUD12uStQBgGBRrAbmlxuGNer5DSeoih9Xo1VPkPXGBCvqTd47DqmIAKE86WWv5hYusixnOZQCGI3VxOO8Cwk6yk7WKL0yb+GStZr5krelG0WItyVoAg5A6jnZLQJxqKNYCgGFQrAXk1m+y1nRqpZxkpsoplqxl8gz6kTtZy7ESAEqT6leLboOofwYYjvQ2iL1Pb2cVFQ0mWauafUbeeZ2lr0k6WSs7sWWpVD8rWQugP6njaLdtELNSK22DCADlUawF5NZsdh6I122DOHGKXHQyeQb9SR1bl5LcAQDlSZ2DdDpnydwG0VgYYCiaqQWEGekf3dRqtXShUA+pTt3mUqo6/5W3iGxh+1utdjKxpXiylsWdAIOQTtbKLqqdyiiElqwFAOVRrAXkllo1kT9ZyzaI46LIeyKWHvqTd0VaVSf2AWAcpca7nc5ZslaeGwsDDEfqvKle671YKyJiJlFY1EsxbtdtECt6Tpe3L1vY/qzHzKwqdskhWTBX0dcLYFykinG7Jmtl9K15dwgAALpTrAXk1mqnIucla02aQtsgVjTGH8ZF6ti6lOQOAChPkW0Qp+q1SJ3y6J8BhiN13pR3AWFKqrCol1SnrtsgVnD+q91u598GccFrkvWY4slaFncCDEKRNOGFGhmplWq1AKA8irWA3CRrrRyStWB4cidr+VsDgNKkxrszq5ZvCZK1Tdb6poULAMMwl9g+Pu8CwpTUVlC9zFWNY7LWXKsdOdcPRasdMfer89KsYrZNJGsBVEKRNOGFsq73SNYCgPIo1gJya/Y5MZacAGu2omWv80oplqzlBA36kTq2LiXFDgDKkxrvptJAUucyxsIAw9HscwFhSpmFQt3SuHpJ6xq0or/nxvtnFZ5NNzr3mSmS+AEGo+dkrYy+NdUfAwDFKdYCckulv+Qt1spasSGdqVqKTIh576A/eZO1/K0BQHmSyVqJc5Z0spb+GWAYUudN9b6TtcpLge82l1LFZK2ibdp4/6zfNbW1ZPL+yfegesVtAOOi3W4nz1W6JWs1alnJWoq1AKAsirWA3FKrJvIna6UPOVbLVUuR1Z7SBKA/eVek+VsDgPIkk7US5yzJ1A/9M8BQjEeyVvZjqjj3VTTtK0+yVrfEluX3T6RXVvD1AhgXmUW1iePuRvV6LVL1WpK1AKA8irWA3Jrt1MRYvkPJJhkr66oYBb+SFdoG0XsHfUkdW5eS3AEA5UmNYVMXmNNbNBkLAwxDvwsIU4aZrFXFPqNo0fHG3yHrd+2W2LJU1jaI7ZznywAsljWPmKeoNlUMrVgLAMqjWAvIpd1u9z0xNt1Ir9ioYhT8Slbk/fDeQe+yjq1LzW6o3sQ+AIyr1Bh2OrHKPPV1qR8AwzH8ZK3i51/d5keqOH9SdFHQ/DaIGeen041ilxyyirs2NBUFAPQiqxg3T7FW6pqPYi0AKI9iLSCXrEH4VCPnNoiZyVrVm7BayYola3nvoFdFJjgkawFAeVJj2OLJWvpngGGYa3U+3jYKFgYtldoKqrdkrewCryr2GcWTtX61DWLi/LRRr8VUwfckazuuKqaRAYyD7GSt7G0QI9K7qcwp1gKA0ijWAnLJGoTXUxuYL5G1sq6KqwtXsvUFJsO8d9C7IhMctoAAgPKkk7U6n7Okvm4sDDAcybT3nHNSKam5ql4Kq7r1CVUs1lrfLFYMNTufrFWs6DlLVrKWfhagN5kJiDmO1angymaieBoAKE6xFpBLZrJWzsh5yVrjo1iyllWO0KtWgeKrdtsWEABQFslaAOOlmTh36ncbxNRcVS9zHd36hCr2GcWTtR56XVKJLXkKAJbKKvCq4msGMA6yjp95CmtTKYmC/wGgPIq1gFxSk2IR6f3Ll5KsNT6KvB+2ZoPeFY0O9/cGAOVIjXdnVnXeEiR1QcN5DMBwNBMLV/LOSaWUeXzv9pgq9hmzBc8xN/4OqWI2yVoA1ZB1/MxTWJvqX1PbEgMAxSnWAnJJTYpFREw18k2MTTXqyUG+dKZqKZSsVXAVJvCwrGNrJ1kR5gBAPq1WO50IklhgMjPVuYjLeQzAcKQWuuSdk0pJXbDuJdGpW59QxT6jeLLWQ/dPFj0n+ssskrUAytd3slbiOk7WDiwAQDGKtYBcstJfGrX8E2Opix8KfqqlyASiiTPonWQtABi+rP40tR2WbRABRit1cbheYE6qk1RxUS+JTt3mtqrYZxQtIJudT9YqcRvERKrlQz+negVuAOMg1Y816rXkFodL79dJ0blMACBNsRaQS9aKiSKR86mLHwoQqqPdbhfbBrGCk40wLoquRlPYCgD9y7pYnlpckrr4bCwMMBzJZK0+t0EsM1mr29xWFfuMom1a3zVZq4dtEDOKBqr4mgGMg1Sxa9Yxd6HUNR/JWgBQHsVaQC7NdsY2iPX8h5L0inQr5apirtWOIudc3jvoXdaxtROFrQDQv6zx6yaFk7WMhQGGoZWYqCiygLCT1PF95SRrFd0GsZn5uF6StVY1apEKSKviawYwDpJFtYnznaUUawHA4CnWAnJpNjOStRoFtkG0Ir3yir4XJs6gd1nH1k4kawFA/7LGu9ONzlsxOY8BGK1kslaBOalO0slaxYtxuydrVa/At9dkrdkNnX+XXpK1arVaMulFPwvQm2RRbc5krVRypWItACiPYi0gl7lWenKkSOT8zFTnix8Kfqqj6Hth4gx6l3Vs7UR6BwD0L2u8m1pp7jwGYLRSF4cbBdLeOynz+N7tfK2KfUbxZK1fFWslCtOmE69nNxIsAcqVOn7mT9bqfL9U8TQAUJxiLSCXrBUT9VRWeQdWylWfZC0YnqKr0RwrAaB/Wf1p6mKxZC2A0UotdGkUmJPqpMxtELs9pop9Ru/JWonttXpI1opIF3mZcwLoTer43n+yluMyAJRFsRaQS9aKiULJWomVGyZfqqPoqkXvHfSu6Go0f28A0L+s/jRVlJVO/NA3Awxau92O1KlTo8CcVCfpbRB7SdbKfkwV+4zic0AP3T+15WPq9exGPwtQrtTxM5UouVQ90b9K1gKA8ijWAnLJSn9pNPpP1prdINa8KopH4HvvoFdFk7VMVANA/7LOPVLnK+mL+cbCAIOWdd40VWBOqpMyi4RSaVMPP2f1+ozCc0AbBpOspVgLoFyp42feotrUAv2WYi0AKI1iLSCXzImxQslaiVjzxIo8hq/XCHyguOLFWtWb3AeAcZNKA2nUazGVKNZKrUA3FgYYvKwUj36TtdJFQsXPvVL9y/z3K9hnFJ4D+tXvmPpd8ya2LGW7YYBypZO18l0WTvWvkrUAoDyKtYBcsgbh9VqBYq3UJFiX1YcMTy/bILbbTtKgF0UnOExUA0D/ekkDkfgBMDqZae8F5qQ6KbMYt1tqfBX7jMJzQPPJWp0f13OyVmpxpwVLAD1J9WOp4+1SqQX6RReeAgBpirWAXMpK1kqulJOsVRm9TB5uaDpJg17YBhEAhi917pG1JYjED4DRGWSyVnqb2+IL01ZSsla/iS3LHpdItqziawYwDlLFrqlt35eSrAUAg6dYC8glcxVjkW0QJWtVXi/FIFY6Qm+KFmuZqAaA/qXGrpK1AKqplbWAsDGYbRAjii9M6za3VcU+o2ibNvahqXPTrMLnLDOr9LMAZUona/VXrJXVJwMAxSjWAnJJFRQ06rWolbANomSt6uilGEQBCfRGshYADF/ywkViK6wIyVoAo5SV4lEk7b2TrGN/4S0CV0Ky1tzGZK1yt0FMJb1U8TUDGAfJBMScyVqp/lWyFgCUR7EWkMtcq/PgvmjcfGoSbHaDZKaq6C1Zy+QZ9CJ1bE2RYgcA/UuNXbPSQFLnMeubLavLAQYsO+29v+ntrGN/kbmOdrvdtbCoiudzxZO1Hrp/L1sKZ0kna1XvNQMYB8lirT6TtYouPAUA0hRrAbkkk7UKpGpFpCdtFPtUR6pwLqsuz/sHvbENIgAMX2qbqqw0kKyLz1KCAQYra5FL0XmppbKO/UXOv/LMi2xotitX4Jsqhkq9rBv70HRfmk4qy5JK1uq2tSQAna1PHN9Tx9ulphLF0EUXngIAaYq1gFxS8bZF4+aT2yAqQKiM1MWm1ZusSj/G+wc9KRodrjASAPrXSxpI1sV8/TPAYGUmazX6K9YqK1krb+Fu1Qp8U/M5q2emOn5941aPqd+j120QsxIsASgunayVr6i2LlkLAAZOsRaQS2rlX9FJseRKObHmlZFatbh6k84TdRHeP+hV0VXVCiMBoH+pJNmsC8zZxVrGwgCDlHVhuOgiwqVKS9bKmQBVtaSo1MX81IK9jX1o6vfodRvEZBJ/xV4vgHGR6sPyJ2sp1gKAQVOsBeRSWrJWYk90K+WqQ7IWDE/xZC0XgwGgX7PJZK30KvOsbZ2MhQEGKzNZq89irexkrfznX3nntWab1TqnSyZrJRbsbfw9U69Nr9sgJpP4zRcC9CSZrJWzqDbVvxadywQA0hRrAbmkJsaKToqlJm2slKuO1Hux+XQjaom329Yv0Juiq9FcDAaA/qXGu5nJWolFJxHGwgCDlnVhuJGaqMiprGLcVGrj8vtVq89I9WGP2DSVrPWrbRBTiS29boOY6GctWALoTeo4nXVes5BkLQAYPMVaQC6pibGik2KpSRsr5apjfWKV5yarGumVji5QQU+KJ2v5WwOAfqXOPbKKtbK2CzEWBhiszGStRn/FWqsatVIWpuWd16ra/FeyWKtrslZ/iS1LTTc6F83pYwF6kyp2zbsNYl2yFgAMnGItIJdmq/PkSNFJsdSkTdVWFq5kqfdieqqePJmz0hF605KsBQBDlzXeTZGsBTA6WcVaqeSPvGq1WnKuo1iyVs5tECs2/5Waz1m9SSpZqxnNVjt5sb78ZK1qvV4A4yKdrJVvu9pU/1p0LhMASFOsBeSSWvg3VS92GElN2ij2qY6s1ZGpkzmTZ9AbyVoAMHypc4+srbCyVqDn3foKgN5kboPYZ7FWRMbCwgJzVXnP1ao2/5Vq9+pEstbsXCuziK33ZC2LOwHKlDq+503WSvWvkrUAoDyKtYBckslaBSfFUhdApMVUR+q9yE7W8v5BL1LH1pSqTewDwDhKrjLPuMA81agnz32qtqUVwKTJTtbqf3p7OjFXVWgbxJz3rdL8V7vdTrYnVay1vtnKPC/NKnzOkkrW0scC9Ca5IDsjMXihVLJWVp8MABSjWAvIJbViolErVqyVTtYy+VIV6aSBulh6KFnR1WhVmtgHgHGVlSSbReoHwGjMZSxyKSFYKyNZq8A2iDkX1lRp/iSrECq1DWK7HfHA+vTv2us2iOk+1oIlgF4kF2TnTtbqfD/JWgBQHsVaQC6pFRPFk7XSg3yrMqohNVmXlaylgAR6U/S4V6WJfQAYV1lJslmkfgCMRtacVK3gIsJOyijWGsdkray2pJK1IiLuW7ch+b1et0GcWZVI4tfHAvQkuSA7cbxdKlXTVXSXAAAgTbEWkEtqYmyqUU6yVkS1JqxWslQywMxUI3kyZ2s26E3RYi3HSQDoX1aSbJb0luDGwgCDVNYCwpTUXFWR86+8hV1VWoCT1ZZUslZExL0PziW/12uxlvRKgPK0Wu3Y0Ozcd/abrGXBPQCUR7EWkEtyG8SSkrUiFCFURWrV4sxUPfn+ee+gN0Wjw6s0sQ8A4yo93s1eZZ5M1tI/AwxUcgFhScVaZSxMy52s1axOgW9Wmx/Rc7JWvsSWZY9L9LGzkrUACstKJUwdb5dK9bGKtQCgPIq1gFzKmhjLmrSxIr0aUqsWpzOKtRSQQG9sgwgAw5c13s2SOpfRPwMMVnIBYQlbIEZEzCRSRoola+Wb06pSUlSvyVr3rUsna3XrS1OyFge22woDAIrIOr7nTUBMLdIvuvAUAEhTrAXkkhqE1wtOjGWdDLjIUQ2pVYszUw3JWlCy4slailoBoF9ZSbJZUluGGAsDDFZyG8RGWcla/S9My3vfrLSTYRtEslbZxVoR1XrNAMZB1vxh3mItyVoAMHiKtYBcmq3OEyNTBSfGFGtV3+yGzidz01P15KSbAhLoTerYmuI4CQD96zlZq4SL+QAUl1rkUtY2iKli3CIpWHn7gmola6XncrKSte5NJGtN1WvJJJZuspP4q/OaAYyDrL4m73a1yWStpmItACiLYi0gl9Qitka92GEk6wKIgp9qSK2snJmqp7d+qdBkI4yT1LE1K7nDFhAA0J/UeUe3Cxfpi/nOYwAGqZVK1iqpWCtVjLu+mf/4nrtYq0JzX9nbIGYla3Uu1sqb1tJJ5nyhOSeAQrISCfMmIKb62JZ5SQAojWItIJdkslbBibGsCyC2D6mG1GTd9FQ9XUAikh56kjq2bjqdcaz09wYAfUmdd3RP1kosXNA3AwxUOlmrnKntMpK18s5pVWnuK9WWWi1is4xz0tQ2iKl+Mg/bIAKUJztZq79tEFN9MgBQnGItIJfUILxeK1aslZ2sZfKlClLvw8xUPb31i1WO0JPUsTVrYrxKk/sAMI6yxrtZyriYD0BxqUUuJdVqJRcWFikSypuYVaW5r1Sbpxv1qNVqyX4xtQ1iqp/MIztZqzppZADjoJxkrc73ayrWAoDSKNYCckkNwosmazXqteRjFCBUw/qMbWFSE3VWOUJvUsfWrGKtKk3uA8C4abbayWLpbsVa6W2y9M0AgzToZK0yFqblndOq0vlcqs0b+8NUv5hO1ur9/chM4tfPAhSSVeSat7A2dbe5RAE1AFCcYi0gl9TEWKNRrFgrIj3ZU6UJq5UsK2kgtfIm7wpSYLF0sdZU8jEKWwGgd1n9aNdtEFNjYclaAAPVSs1JFVxAmJK6cF0sWWv8irWS8z+/2s5wOlFAdd8AkrWyCqb1swDFpPqvqXotpnIXa3W+n1otACiPYi0gl9TEWNFkrYj0RRAFCKPXbreTJ3MzU/X01gDeO+hJqlhrU8laADAQWePWrFSPh74vWQtgFNLJWuUUayWTtQosTMs7L1Kl+ZPUueXGoqthJmtlFUzrZwGKSRW55t0CMSLdx0rWAoDyKNYCckkma/UwMZa6CCKdafQ2NNvRTmw7P52ZrOUkDXqROrZmbYNYpcl9ABg3Wecc3ZO1EucxGduMANC/1CKXeq2sZK3+F6blndOq0txXOlmrW7FW+claU/VapKYYJWsBFJO1GDuv1HWfVJ8MABSnWAvIJTUIb/QwMabgp7qyJg1nphq2foGSpY6tm2dsg1ilyX0AGDdZ5xzdLl44jwEYjWSyVmPQyVoTvg1ioth4Y9FVqt9LFWt1S6jMUqvVMvpZ58AARaSOm+UkaynWAoCyKNYCcilzYiy5fUiFJqxWqqz3ICtZSyQ99CZ1bLUNIgAMRj/FWs5jAEYjuYCwpG0QU4lQRRamjeM2iMnklVUPnY+m+r37ZxPFWn1sg/jQz+s/4QyAdP9VpKi2nkrWairWAoCyKNYCcmmVODFmpVx1dbt4ZesXKFfq2LrJqnqkggtNVANA77olyWZJXsx3HgMwUKlirVTqR1GpIqMiC9PyJ2tVp89IXsxvbNwGsVhSVj/bIEZIsAQoS6r/KiNZq9lWrAUAZVGsBeQy1+o8wJ+qFz+MWJFeXZK1YLiyjq0uCANA+bLGu90SQcq4mA9AcalE4rKStcpYmDZZyVrZ2yCmbEzk6pX5QoBypJO18h/XU32sbRABoDyKtYBcyoyct1Kuurona/W/NQDwsKxjq4lqAChf1ni3WyJIGdtkAVBcM7HIpbRtEEtYmJZ3UU2V5r5S/df0fLJWsUsHg0vWsmAJoIhykrU63zc1lwkAFKdYC8ilzFWMqRWLChBGLztpoJEu1pImAD1JHVun6rXkquQqTe4DwLhJjXdXNWpR73Juk+qbJWsBDFZ6Tqqcqe0yFqaNZ7JW5yKojcla3RInU4/rVTLhrEKvGcA4SCVDFinCTXWxzVY72rZCBIBSKNYCcikzWSs5CWbyZeSyVitONzK2QZxrOUmDHmQdW9PbIDpWAkCvUv1onjQQKbMAo9HKWORShmSiU6FkrXz3rdL5XHqbrIeKpoomZfWbrGW+EKAcqf4rVRTbSSpZKyJCuBYAlEOxFpBLqqCgl4kxsebVlZoAq9UeShvIOqEzeQbFZW6DmFiV7G8NAHqXOudIpWYt5DwGYDTKTHvvJGsL+rwL0/IXa1Wnz+hWwFzkon5E/8la6X7WOTBAEcltbgska2X1sXOJ7YkBgGIUawG5DGMbRJMvo5eK459u1KNWq2VGJdv+BYrLOramViVXadsMABg3WePdbmznDjAaZS4g7KSMuY6x3AYx0ZaNRVdFLupHFC/uWv5458AAZUhdZymyDWJWH5vqlwGAYhRrAbkk019qZSZrmXwZtWTSwFR90f87Ptb2L1BY1kWHVMJHlVZiA8C4SV64yJEG4jwGYDRSi1zqpRVr9Z8invc8rUp9RqrNDydrFS3WGtQ2iM6BAYpILlApLVlLsRYAlEGxFpBLMv2l0UuylpVyVZWMwP/VxGXWCZ1kLSgunaxVj5lEwofCSADoXX/JWp3vM9dqW10OMEDN5mCTtbLmOvKcf7Va7diQaONSVZr7Ss3j9J6s1W+xlgRLgDKkF2TnT0DMKtZK9csAQDGKtYBcmol9yHuZGEuvlDP5MmrdIpIzV5tusNIRimplJmslClsVRgJAz5IXLvpI1opwIRlgkJrt9PbxZeh3G8Qi52hVKvBNFaJNNx6a+ym6rWHR4q68jzdfCFBMcpvbsrZBTPTLAEAxirWAXFLzTo168cNIOllLsc+oJU/kVnWPwFdAAsVlbeeRLGyVrAUAPUtfuOh+QTpzLOxCMsDAZG0fX4Z+F6YVLSaqSp/RLVkrTyHzoscNaBvEqrxeAOOi24LsPDKTtSpSdAwA406xFpBLmclaVspVV3IbxEb3CHwFJFBc1rE19fe2vqmwFQB61W28myXr4kYqsQuA/qW3jx/8Noh5FqYV7QOq0mekk7Xqi/6f1+CStarxegGMi+TW74WStdL3TfXLAEAxirWAXLLSX4pKrVhU7DN66W1hNkbgZ12g8v5BUVkXHRwrAaB8yVXmOdJDMpNXjIUBBia1yGUY2yDmOf8qeo5WlT6j29bAxZO1im2buPzxFncClCF5fC+SrNXISNZqKtYCgDIo1gJyKTNyPjXZYxu90UtuC/Or1ZRTjXqk3nKx9FBc1rE1tYrZsRIAepdcZd53spb+GWBQ5hIXhbNSP4roN1mr6DlaVeZPum0NXDhZq+D9Uz93qaq8XgDjIrnNbYGi2kYtfd1nLlFEDQAUo1gLyCVVUNDLKsbU5I1Y89HLkzSQTPvx/kFhqWNrvV5LFrZK1gKA3nVLks2SuSW4sTDAwLTag90GcapeSy5MG0yyVjX6jOTWwFMbk7WKJWUVTeJK/dylFEQDFJPc5rZIslZGH5vqlwGAYhRrAbkMJVnL5MvI5UkaSJ3Uef+guF6StaoysQ8A4yh5YTpXslb6orWxMMDgpLaP72VOqpNarZZRKNT9/KtoslZVio/SyVoPvRZFk7IGtQ2iPhagmOSC7ALFWll9bKpfBgCKUawF5JIagPeWrJVKZjL5MmrppIGFyVpWOkJZso6ttowFgPLlSZJNyU7W0j8DDEpWInFZ+tmCb3ZDsQU1VekzuidrFdwGsUARQJHHW7AEUExyQXaRZK1GRrFWYntiAKAYxVpALmVug2ilXHWlV1U+PGkpGQ3Kk07Wqqe3HLUNIgD0rFuKSJZGvZZcYW4sDDA4qYvCZSVrRfS3MK3ogpoq9BntdjvZ7o2vRZEEll7uv/zxvRfMAfCw5ILsAgmIjVq6j03NZwIAxSjWAnKZS0zg9DIxll4pZ/Jl1PJsC2NrNihPViFscstRyVoA0LNuKSLdOJcBGL5mu7wFhCnJ869cyVrjtw1iVht6LdYaXLLW6F8vgHFSSrJWRh+b6pcBgGIUawG5pBZLNOrFDyOpyZ5mq50sCmM4UhOMi7dBtI0llCWrWCu5sluyFgD0LLVVVd5V5unkFQsXAAYlnUg8jGSt7sf3ovMhRbdNHITsYq3Gov/n1X+ylmItgDIkt34vcJzO6mMlawFAORRrAbnMtcpL1ppZlZ7skRgzWqnXf1GylskzKE3WsVWyFgCUr9uWT93Yoglg+OYSF4XrpSZr9b4wbX2zWPFVFc7psvqtjX1i0aSsfpO1Un2xPhYgv2arnew3ixRrZSVrpbYnBgCKUawF5JJaLdHLxFhqG70IiTGjltzPflGylmItKEt2slbiYkEFVmEDwLhKJsnaBhGgspolLiBM6Weuo/A2iBWY+8pKDJvucRvEoklcqZ+7lPRKgPyyClyLFNXWarVkwZZkLQAoh2ItIJfUaozekrXSh54qrC5cyZL72TcennBLpv24QAWFZR1bJWsBQPmSSbK5k7WMhQGGLZXg0aiXN7Xdz1xH0XO0KpzTZSdrPTQHVDQpq/9tEDsXe21otqOlMAAglzzH97watc7XflI7BQAAxSjWArpqtdrRTsyJZMXhpkjWqq7kfvaLkrVSWwNY6QhFdDu2Jld2O04CQM+SSbJ9J2sZCwMMSitx4jS+yVqj7zOyfq+Hk7WKXdTPmu/L9fiMvrgKBW4A4yBPcmJeqWs/qX4ZAChGsRbQVTNj8F1+stboJ6xWstTKm4WTlqn3T5oAFJN1bM0s1vK3BgA9S493812QlqwFMHypROJeFhCm9LMwbfKStYpvgzjdqEe9z/cj6+c5DwbIJ+t4WTQBMXXtJ5V4CQAUo1gL6CprD/JeJsZmGukLIeskxoxU6mRu4aqbmcRKSRNnUEy3Y2vWNohtK9gAoCd5xrtZ0slaxsIAg5I6dyq3WKv3YtyiSVlVSEvOk6xVJIGlaFpLJ9nFWhZ3AuRRZrFWo9G5n82a0wQA8lOsBXSVWsEY0WOxVkaylosco5WaYFy4wjT1/nnvoJisY+tUvZ6Z8OHvDQB6k7pAnvfCRTp5Rd8MMCipBI9hFGvl2gaxYB9QhT4jVfxUrz2cpDJVr0Xel7hoAUDn58g4B65AgRvAOMgqbi26vW2jlkjWUqwFAKVQrAV01cyIte1lYmw6kcwUYfuQUUtF8S9cIZl6/9Zb5QiFdEvWyprsrsK2GQAwjvKMd7Mkky+dxwAMTCuRLJzanqkX/RzfixZfVeF8LvV7TU/Vo/ari/O1WjrxudPj+pX1HFV4zQDGQVa/VfRYnbr2k+qXAYBiFGsBXTUzBt9T9eKHkXq9FqsSEbpizUcrT9LAzCppAlCGfoq1rCoGgOLmmq1k/5t3lXk6ecV5DMCgpBI8hpOs1f34XjhZq+C2iYOQavPS/rDf/rEI58AA/cuzzW1eqaLoVOIlAFCMYi2gq7lWeoDf68RYarLHivTRmu0rWct7B0V0O7ZaVQwA5crqP/NeuLANIsDwpQptyyzW6idZq+h8SBXO51JtXlowNcxkrZlVzoEB+pU6vq9q1Ar3m43EgvusBagAQH6KtYCuuqW/9CI1ieMix+i02+1ck3Xp1abeOygi69g6Va9lrmCuwkpsABg3WakceRNBnMcADN9colCnzG0Q+ynGLZquWIWUqFSbl/ZzefvHvAlcWVKLAyOcAwPkleq3so6xKaldVVKJlwBAMYq1SnL33XfHox71qKjVanHKKaeMujlQqqxY214nxlKTPdKZRidrleLCSbfUSkfvHRSTdWzttg2iVcUAUFz2eDfvxWhjYYBhSydrlTe13c/xvWgfkEo1H6a8yVr99o9FTDXqyUWhzoEB8kkV486sKl5Um7r008zYLQAAyE+xVkmOP/74uPHGG0fdDBgIyVorQ9ZrP5NjG8SiK0lhpWu1MwphG9nbIFZhJTYAjJus/jP/NojOYwCGrZk4dyozWaufeaqifUAVzueSyStLErKW3k4pYxvEiIw5pwq8ZgDjIFWMW2aylm0QAaAcirVK8H//7/+N//zP/xx1M2BgUpNiEYNI1lLwMypZK0EXbYOYWIUjTQCKyYoMb9Syk7VcEAaA4rIWF+Tdvsl5DMDwpZO1ytwGsfeFaUXnQ6qQEpU6pxxlslZERpp7BV4zgHGQPL4njq9ZUv2sbRABoByKtfp00kknxfvf//5RNwMGSrLWypD12k/nStby3kER3Y6tU416Mm5ccSQAFJd3vJvFeQzA8KUuCpdZrJVKkMpz7pUq6Eq1b3bD6At808la9czbKQNP1lIUDZBLqclajc79mGQtACiHYq0+/OVf/mW87W1vG3UzYODmmuUXa6VWrrvIMTpZk4UL36/UKhzvHRSTdWzdGDOePlaaqAaAovJu+50l2TfbnglgIFqtdqQC34eTrNX7NoirN5nq+TkHLXVO2XuyVr6Eyq7Pk5pz0s8C5JI8vkvWAoDKUazVg9nZ2Xj9618fJ554YkRE1Ov1mJ6eHnGrYHAGkqyVWMkhLWZ0siLl8yRree+gmMxj669WrqVWJ/t7A4DisvrPvCvNk32z7ZkABiLrgvBUqclavZ97pe6TKtaqwvlcqg29FmsNOllLPwuQT5nJWo2aZC0AGCTFWgXdfvvt8eu//uvx8Y9/PCIiVq1aFaeeemrsvPPOI24ZDE4ztYQxHk5/KSqdziQtZlSyVikunJybWSXpB8qQdWzdOBnSz+puAGCx1Hh1ulGPes4L/um+2VgYYBBaWedNVU/WmlmVuP/o+4xUm5cmZOVNzMpb1NX9eSRYAvQj7/E9j1Q/q1gLAMqhWKugK6+8Mi655JKIiNhmm23izDPPjFe84hUjbhUMVrOVnhDpfRtEaTFVk7VKcVGxVuK929BsR8uJGuSW59iaKmx1rASA4vKmiGTRNwMMV1ayVqnFWomFaQNJ1qpASlTZyVqD3gaxCq8ZwDhIHt972AZxqqFYCwAGSbFWjw499NC49NJL4xnPeMaomwIDN9ccwDaIqZVyLnKMTGqVYr0WMbUgJjkr2t7kGeSXdWzduJ1HKqK8CiuxAWDcpM41imzdNN1wHgMwTM0BzEl1krX9XreFaanzs9WbJJK1KpASlbdPzNtHDnobxNkNzoEB8kge33vZBjGxq0pWITUAkJ9irYI23XTTeP/73x9nn3127L777qNuDgxF1kqJXufFJGtVz/pm54mv5RH46a7DRSrIL3VsrdVifium5BYQ/tYAoLBSkrWcxwAM1VxGIvFU4iJyL7ISR7otTEv1AY+odLJWag6o12StkrZBTCSczVbgNQMYB6kC4tTxNUsiWCtztwAAIL/OZ4wkHXTQQXHQQQeNuhkwVKmVElP1WtRqvSZrpdJiDPRHJbWyc+l7lV2s1YyIzitHgcWa7fSxdSPHSgAoTynJWvpmgKFKnTdFDCdZK+KhY/wmGRe5U31AahtEyVoZz5NM1hr9awYwDoaRrKV+FgDKIVkL6Co1MdbPpFiq4MfWXqOTOpFbvqoyY4LS5BnkliqErS8ogk0fK/2tAUBRyVXmGePb5fdNXbBox5yrFgCly0p7nyqxWGuTjGStrLmqZqudPLdLboNYgbmv1PzN8nT1fH1keclazoEB+pGc48/o51JS/axkLQAoh2QtIOlHt9wTnz7v+vj8xTd3/H4/k2KpFXfn/fSuePZJZ8/fbtRr8Wu7bRnHHbpnPGan1YV/zhmX3xqnX3ZLXH/nL3tua8oe220WL3ziLvHcx+9c+nOn/PDmtfGZ826Iy2+5JzIWl/bkngc3dPx6kVWVr/rXC5OrdFZN1eLA3beO1z19r9h92816b+iA/OQX98anzr0+fnrHA/G0PbaJN/7Go2PzGd0kg9Ns9p6sder5N8SZP/pFrp+z6XQjDtpzm3jDEY+OrTefLt5QAFaUVqsdnz7v+vj2T26P2++dTd5v351Wx+8+Zbd4+j7b9fXz1m1oxr+cfV1875o7k+PRer0Wj9/lEfGqQ/aIA3bZsvDP+PaPb4v/+P7N8bXLO/edZSRrRUQ89+++u6joOsue220eL3zSLvGcA3bK/bMBxslcsxWf/N7P4jtX3RFrHljf8T61WsTjdn5EHHPQ7vHUPbZJPM+wkrXSRUkv/5fzY1UiXaSVMTmTStZqtSN+86Szohad29/pHO7a2++Lfz3n+rjsxrWZBWx53bDmgY5f7zVZq7RircSc0ld/+PP4/g1353uOVfU4cPet4/WH7xWP3GrTUto1KBuarfj4d6+Ls6++I+5+oPM4qFaL2P+RW8bvHbx7HLj71kNu4WSaa7biE9/7WZx99R2xw+qZePnTdo+D99o2Ih7a1vTj331obLrzlpvEMQftHk9JHJ+gKlqtdnzm/BviWz++Lb57zZ0d79NTslZiH8RUkTKT7ZtX3hZf+v7Ncd0dD48hphoPXfN57dP3jD2223yEreOu+2fjn8/6aVx209p43M6PqOx1uJXkjvtm42Nn/TQuun5NrEsslPjEq58Su23jfVrJXIUeI7fffnvccccdhR5z7bXXDqg1TLrLb74nXv4v58UD69Or/ep9JWt1ngR7YH0zrrrtvkVfu/LWe+Nrl/8ivvCGQ2LfHfMXbJ12wY3xp1++vOc2dnPVbffFmVfcFn/1osfHMQftPrCfs9FlN62NYz5+fvwy4z0ZhOXJWukTu5/d2Xmyb6Mf3XJvfOOK2+JLbzq0UhNmP/nFvfE7/3xe3LtuLiIiLvzZmjjvurvic79/cGlR/rBUamJj4QWH1LFyzQPrkxc8OrnsprVx1tV3xBffeGhsoQgRgAzv+a8r4lPn3dD1flfddl987fJb45+PfXI8a78de/pZ7XY7TvjMJXHW1d3Pc3/8q3OCz/3+wfH4XfMXbP3nZbfEW/79ssyFDkUuMGcljFxz+/25n+eq2+6Lr1/xi/iblzwhfuepu+V+HMC4+OMv/jC+fOktXe/3k1/cF1+9/NY45finxqGPXl4AnFWYVGaxVlbiyMKLkkWkkrUiIq6+LbvPWHgOd/u96+J3PnZ+oXPAXhWZA1p8v/wplZnPk3gf7l03F/euu6/j9zr54c33xLd/clt86Y2/HtuvnimlbYPwR/92abKYfKGH/k5+Hqe+9iCFQyV42+d/EF/5wc/nb3/18lvjk69+ahy2z3bx5tO+H9+48rZF3/vUa542X8wFVZTnHK7cZC3FWivNl75/c7zt8z/o+L0rfn5vnHnFL+I/3nioopMRuXfdhnj5v5w/Pydx0fV3x5lX/CK+9KZfj10qdB1uJbnnlxvid//lvK7nERsktK94rkCPkY985CNxwAEHFPrvhS984aibzZj6zPnXZxZqRfSXrFV0xd09D26If7vwptz3b7fb8dGzhlOs+M9n/TTaZcdcdfDp864feqFWRO8R+Cm/uHddnH5Z9wnbYfrcBTfOF2ptdMkNd8fF168ZUYtYCVITG1MLVrr1MpGS8pNf3BdnXVWs6BuAleWeBzfEqRfcmPv+zVY7PvHd63r+eVfeem+uQq2N7p+di1PP715IttA/n3Vd10TaIv1tmX1zRMQ/n/3TUp8PoApuvefBXIVaG62fa8XJ51zf8XtZ6R1lboNYVjLUQo/YtL+FMhvP4f794puGUqgV0UexVkn9Y1lFXxERN615ML52+a2lPV/Zrr/zgVyFWhut29CKU869fnANWiFuvvuXiwq1IiI2NNvxr+f8LK6784FFhVoRD20pd0ri+ARVkPccrpfjayORGixZa+X52FnZ5/233zdbaOxHub7949uWLR677d7ZON17MjLfuPIXPS/4YGVRrAV09IOb7ul6n6026307rW162IrrBzevzX3ftb/cEDetebDwz+jFjWt+GWt/2TmqvEw/uGntwH9GJ1tvvngl6MxUPTZd1d/k2WU3ru3r8WW77ObOn/dLR/SaszI0E1eOF26ftE0fx9lOLrsp37YRAKxMV/z8nsKrpC/rY7z0w8QYrKyft36uFT++9d6u9ytyXrPVpumUlF5cd8cDcd+6wZ9LAAxTnjmlpVLH96xtBstM1tpseqqnLaKy7FnCdkCX3XR3/LCH17NXWy+Zr1t6O6WfOcJFP6/0c+C1pT5fmYrMc25U5d9nXKTGn5fdtDY59+p1p8rynsNtvVnx85hUP9tSrLWiPNhhN5xOHCtHJzX29p6MTi/jPFYmxVpAR+vmuic4PX3v5fH0efXy2NkcbdooT/vLNDs3+KjK1J7Gg3bYPtsvul2v1+Lp+/T+3kcM5/UqYnZD589L1drJZGm2On++Fq4OP6zPv7WlfKYByDLbw3hzdq7Vc8rsusQYLPvn5X9M3vseXqC/3XaLmThgl0fkvn8e+mdg0hQ5Vs8/JtEnzDWHU6w1PVWPgx9d3jZn+z/yEbH/I7eMfXfcoq/nmZ1rDW2Oq1aL+PUl83W/vvd2kQhWmTczVY+nlbQ132H7ln0OPPyE+rx6HXfRn9T4c3ZDKzn3WuXPEeQ9liyd489jqiFZi/zHQMfK0Un2bcYNIzOq67mMH8VaY+RNb3pT/OhHPyr03+mnnz7qZjOmug3yn7DrlvGO33xMz8+/2zabxfteeECpber1vmUYxkB0FAOrZzxm+zju0D2Wff3dz98/9ti29/3HqzZwT722VWsnkyV10WHhBYejHrdjHHvw7qX9zGEfGwEYL72OfdY3e+tfehnfFnlMnvs+/9ceGS85cNdCbfjgy54Y221RXvKHCVRg0pRZhJKVFlJmsVZExP/vhQfErltv2vfzbLfFdHzwd34tIiJO+t0n9pRmstHshtZQzuPqtYgPvPTXYrstZhZ9fbstZuJvX/prkXqpVzVq8Q+veFJsOl3O9oVP2m2reMtR+5TyXBHVPgcus6iR/LLmAF3sZhzlOZb8v6P3i713KF48nOpni6YxM97yHgOr3OdOumTfZtwwMsYO5DU16gaQ3w477BA77LDDqJvBCpEa5D9tz23ij5/9mPi1XbeK6an+6j2PPfhR8YzH7hDn//SuRVt/fP/GtfGVH/y8Q5vKuTDzx89+TGzewyTSA+ub8bdnXlX455Ul9Z68/Km7xWN3Wl3qz2rUa/GEXbeKA3bZsuNJ2S5bbRpff8vhcfH1d8e1t6cjcL937Z3xrR/fvuzrVRuoJJO1nGAwQKmJjYWr1ur1Wrz3BQfEqw7ZIy654e7cJ1j/+YOfx6UdthtVgAhAltQYbfXMVBxz8O7xsbOuSz5uZqr4+Do11tp9m83ioD23iS9ccnPuNqbalfL6w/eKFzzxkfG4nR4R9YIX+x+z0+r4zh8/Iy782V1x412/zPWY+9bNxQe/eXXndppABSZM6rxjuy1m4ugn7BynnHt9h8c8lNRYWxLjNJdIJI6ImKqXuw55t202i2++9Yi46Po1cd0d9/f8HE/bc5tYvclDBVr7P3LLOPudz4gLrlsTN9+d7jOyzuFSr+dvP37neOoeW/fUzoW23GxVHLLXdrHTlpt0/P5Ln7xrPH3v7eK86+6Me3758PzdNlvMxCF7bRvbr57p+Lhe1Gq1eMtR+8YLn7hLXPizNfHL9XO5Hvftn9we373mzmVfr9r800Kptu30iE3imY/bIT57wY25H0N+qXFXqx3xoGItxlDW5/MDL/u1OGjPbWK3bXpbdN1IRCtm9c1MnrzXSBwrRyc1VvSejE7WPM+7n7ff/L+33by8cTTjSbEW0NH6RCf++4ftFU8tKdo84qGin5c8efFK9kdsenPHYq1UmzrJuu8rD3lUPGKT4qsa7123IVmsVaRtvUr9jGfvv1M847HDL+TcZFUjnr7PdplbIm5otjsWaw3j9SoilQbRa0oE5NFMbBm1tECyVqvFvjuujn13zF+Uec3t93ec6PeZBiBLaiLvEZuuipccuGuyWKvXsd36ZufJq9222TSO2m/HjsVaZZ0THP/re8TOW/aenrLFzFQc+dgdc99/zQPrk8Va+mdg0qT6k+1Xp4u1Ih6aQ5ieWnw+1MrYarfsZK2IiE2nG3H4vtvH4fsW3y4qZfUmq+Ko/bL7jGvvSJ/DpfqJw/fdLn73qeUlMWfZactN4kVPKpZE2Y89tts89thu89z3X/vgho7FWlWbf1ooWay15SbxW4/fuWOx1vpm56JG8ssad927YDHxQs1WO+aarZhq2KiG6kkdS3bZatN46ZP7O243EkXRTl9WltR5+7L7VbjPnXSp1957Mjqp8cbvH7ZnHPfrew65NVSZ0SXQUWqQP9NnmlYeqVX5RRJhsu7b6++Q9bhBp9W02+2Rvie9mlnVuW1Vq+hPrQ6RrMUgpZK1UqvWikgeR32mAciQHG+uqncZC/e4DWKiX5qZaiR/XnnnBOVs15RX5uunfwYmTNb8Rdbxt9NxO7V9fETE1ACKtUYl6xwuq7/kIWXMJQ5b9t9J53FDu/1QUSO9yxp33bcuneSmuJ6qyjqH69fC9P+FmpK1VpR1uZO1qtvnTrqsLX4ZDeN38qruFX5gZJqtdswligj63fowj9SERFmr6Kd7XAWV9bhBFx9lTcSUceI1KGW8l8MwK1mLEUhddChjdXjquOAzDUCW1BjtoeKp9IRS78laxS/mb2i2o5U4VynSrmGc1yyUVaylfwYmTbo/qWfOYXR6XGqRS0QU3sa2ypLzJxnJWlVevDds6SLv6vaxyb+TVV3GXcYNfcl6/TKLtSr8WWJlS302e70GslBqjjJ17YjJlLffcZwcndR4x5hhdFKv/bDnoqg+nwhgmaxB1TAmglKdVZEJltR9p6fqPUeF12q1UtrWi6wK+OlGdSux069XdSr62+128jOfta809Ct10SG1aq2I1ISM5A4AsqTGaNNT9cwJpV7Hdql+qdvPyzvhmDVGH/YF7qlGPVI1BfpnYNJkzckUXQiXuiA8SalaERnzJxnJWi72PCz1WlT5wnFy3NXoMu4yV9WXrPHhvQ923gax2+NglFLHkplV/V8zSPW1WYXUTJ6856uOk6OTTNYy1zAyyWOz8TtL+EQAy2QXaw2+MCjVWc212rlPBAa1ZeCokqIy35NKJ2t1/rxUabIs62KflQcMUuqiQ6Pe/990cgtSn2kAMmQmoWQVTw0kWav/VNtUu+q10VzkT46Nmy66ApMlM6mxpGStMhKJqyRrG7/0xZ7qLt4btslK1uoy7nJe35escet969LFWlWay4SFss7h+lVPLHqXrLWySNaqvtRrb8wwOoM8NjNZfCKAZTJTnEaYrBWRf8A3qKrlUU3+ZD1/GZHGg5JM96nQwD2rLVYeMEitduKiQwnXHNLJWi4GA5CWteAhO1mrtzFTVpJXdrFWvv4s6/l7TdvtR1ZqCsAkyZqTKZqslUwknrBirVQf8cv1zUhdE5es9bBRJeH3Iznu6pqsVd3faRxkjSOztkGs0i4BsNCgFq1HpPvavNvSMxnyzidXuc+ddKk+yphhdNJJwxZbsJgzOmCZUW8XkrUyMP+FmfQqzn4kVzoOuAAi8z2pcrJWKt2nQgP3rAGriRgGaa6ZuuhQRrJW9VPtAKie1LhoZqoeU/Va6dv4pX9eI/ucIO82DBnPPwrjmPoB0IusC8dZ2zJ1OgdPpXfUJ6xYK9VHZBWPWJn/sKxksqpK/p10SdYybuhP1jgy6+9tnQveVFTWOVy/GokVpZK1VpbcydbNlkK+EUkdB6o8Dpp0gzw2M1l8IoBlRl2sVUayVup+/a46TD1+0HGiWb/3OCZrNVvtmKtIBKttEBmVZqvz56uM7TxmxiDVDoDqSW3HtzGJKj0W7m0CMDXWmu6S5JV7G4aM5x+F5OunfwYmTNacTNYcRpFtEFdKslbWtmyStR6Wmq+sch+7PpUA2iVZq8q/0ziYzRhHZm6DaI6Qiso6h+tXqq9N9c1MpiL9jmPlaKRe91Y7KnMdbqWp2nwU1eUTASyTVW09jFXoZaweG1T8b3I1/IBXV2W+JxmrUkctq21VGbhnpaKJiWWQUqvQpkrYB3EcUu0AqJ5uSVTplNmyk7W6JEr0naw1mqmQdLKW1a7AZMnaBnFVoxapnWg7na/MJRe5TNa0dqqPeGB91hzdZL0G/chKr2y3q1lUkE7W6pIwatzQl6xxZNbfmzlCqmqQacKpvlay1spSZD7Z3PNoZF7j8p6MROo9MX5nKZ8IYJnMFKdhbIOYsa1f7sjVARVrSdYqpuiK2VGQrMWopFahlZKslVxVbFIXgLTkyr9fjenKHgunkg1mphqZ5wR5f17q+Ue1kjG9RZMxJzBZspK1arVacq5gJSdr9TJfVeXFe8OW1bdvaFazqCD5d9J4qKix6OPIp9dxqzlCqqrbOVw/Uoei1G4BTKYi88n6qNHIvMblPRkJyVrkNTXqBkyK66+/ftRNgNJkXSwYSrFWo//VY6n79dv+0SVrdX7+Wi0yJ3BGrYzCu0HLeu+smmOQksVaqWXmBaSOdVX5uwOgmpKrsn81pit7LJxaadhtm6ysVaN57jeMtOBO9M/ASpFOO9+Y1FjveJ9OczmDXORSJb30TVVevDds3ZKoqnhhLGtXgFqtlvF3YtzQj7zjyLIeB4PW7RyuH41EP2MbxJWlWLKWY+WwtdvtzPfIuGH4Wq12crHAqOajqK7qnaUAI5eqtJ6q14YyGZa5ir7vZK3+OsLU41N7w5clKymsVkJhx6BkrQytSkW/ZC1GJRUZXk6yVupYVd0tIAAYvdTYZ+OYLlmsVXJCwcxUPaYa9WSfmHeMVrWVjOnkS2NOYLJ0SztPJUJ1upi0coq1eknWMrW/URlzicOW/DvpViRf0d9nXEjWYtIMMlkrlWKpWGtlKdKPVrXPnWRzrXZkTfd7T4Yva8xg/M5SPhHAMlkru4YhcxV9zoFF6n79XphJroYfeLJWInWg4qsos17vqqyyyE7WqkYbmUzJ7TxKSMtL/e2129XdAgKA0euWTjud2sav54SC7DF7aqybd+ydXGU+omItyVrAStFtTiZ5fO/wuNQil0nbBrGX+aqqzwkNUxlzicPWba4vOe6qyHzauOo9EbaanyNIHRNKSdZK9LWpvpnJVCxZy7Fy2Lq95sYNw5c1ZjB+ZymfCGCZ9Mqu4cQz1uu15NZ+eQd73VZx9mpUq9qSBXRDek96lZVktq4ikxxZqWhWzTFI6WSt/odnmal2PtcAJKSLmx7etqrj43ocC3dbJJK6wJC3Lxv1IpTlP9dFV2BlSF447nJ8X9nJWsXnd0bVn1VRVlFCVS8cd5vrk8g5GL2Ou6r6OYJB7TASEdFI7OghWWtlKXLc1EcNX7fXXP81fLMZ1xw3kazFEj4RwDJVSHFK/ay8g71uqQC9Sj1+0IPQ5KrUildhj0PBSFaV+4Zm28knA9Ma4ArxzFQ7iXEAJHTbNrDssfD6Lhfz08kr+fqyQS3g6JWLrsBKkTqubUwKKjLns1KKtYrOV0036lFLXERfibLmx6razyb/Thq2QRyknrdBVFxPRQ1qh5GIdPq/+fKVpUg/qo8avm7zI96T4ctO1qp2AAfDV+2r/MBIpFd2De+QkUqMynthZlCr6NMTJYM9Ya/Ce9KLzBj6iiRrdRusVnVSj/GXStaqlzDhnrV6zgkaACndtg0cXrLWrxIlUskrebdBTBaDjWZyzEVXYKXonpyYf85npRRrFZ2vkqq1WFbyfFUTLLvN9dk+eTB63gbR605FDXLr91Rfq1hrZSm2DWI1+9xJ1q1f854MX9bfTNWv6TJ8PhHAMt1Wdg1Dv6vOBxX/m0wTGHBKVBXek15kbWlZlWStbp8pxVoMSrPV+bNVRrJWZqqdzzQACd2StcpMhmq328mf93BxWOexe95xZDrZZUTJWqltHfXNwITpdvydKZCslVrkUsZ5U5UU7Ztc6FlsJSVrVfX3GRe9J2t53ammbudw/Uj1tam+mclU5PjnWDl83fo178nwZb3mVb+my/D5RADLJFegD3EiqN/VY4OK/01dMBp0SlT6Pal+ZGb6NatGRX/3mNhqtJPJk5rYaCQKHIvI3AbRCRoACanx2XzSVWpc18N4aUOzHe3EHP90t20QcydrVWsbxH63dQQYF92TtfLP+aQWuaz0ZC0XehZb1ahFKqS6qufA3eb6yhx38TDJWkyabudw/WjUO/c1krVWlmLJWo6Vw9Y9Wct7MmxZYzULLljKJwJYpgopToNL1urvd6haslZqNWqVjOo1y6vbYNVglkFJTWxI1gJgVLqtyk6O63roW7LGgt22QRzfZK1EUpi+GZgw3Y6/qfmlYsla1Z8PKaJ4slb1F+8NU61WK/S5GrVWqx0bmp0/2xt/jzLHXTys52StisxjwlKDTNZKXXqYSxRSM5mKXB/RRw3f+mZ2Ebf3ZPgka1GETwSwTHoF5PAmgtLJWvlWj6Xu13+yVn+r+3uVfE/GoAp7VK9ZXoq1GJVUsVY9tRy4gOxkLatwAegsNT6b6bINYi/jpayU1e7JWv2dE1QvWct4E5gcrVbWNrfZxbidjtutVCLxxCVrFZtzc6FnufQ4pXrnwJlF66vKH3fxkLlmq+dEoKrsEABLdTuH60cqWUut1spSpB+tYp876SRrVU/qNW/UazFlDM8SPhHAMskUpyEWBvW73WA6Wau/grNRRZAPKilsGKo+udRtZYGVBwzKIJO1sibufaYBSOm6bVWJF0GzxoIPb5OVGHvnTDaowiKUxT+32uNigDLkK0LJP+eT3D5+woq1srbx62QcFu8NW3LcUMF+Nmt+s9t2oc7pe9fPZ6GKnyOISJ8blXHdIDVHKVlrZZGsVW3d5ke8J8M3ztdzGT6fCmCZ1OBrmKv2ktHlfV6Y6TdZK534NehkrcEkhQ1DOra9GqssuidrVaOdTJ7kRYdG/xcdarXayI5XAIyndjudhDKQbRCzYuG7JmuN5zaItjMCVoKs84357d0KzPmkFrlMWrFW1jZ+nUjWWm6cEixnM7Ysmuk2DjJP1bN+xlzGa1RRu90e6DlPqq9ttdPJl0yeIse/Kva5k657spZxw7AN6vo0k8mnAlgmveXe8FagpyPx+03WGsw2iIM+YR9UUtgwpNPIqjFwl6zFqAwyWStCegcAxWQnXTUW/b/IY3v7eV0SJfpO1hrNVMg4bc8E0KusY9omq7KP70WStco6b6qSIv2TZK3l+p1LHKbsZK3scZd5qt5J1mLSZKZZlnDdIKswutlWrLVSFDn+OVYOX7f5EeOG4VufKMqXrEUnPhXAMskUpyoka+UcWExestbo0856VfUEgW4Xx5xgMCjpFeLl/F27IAxAEdkT/aNJ1pqZuGQtF12ByZd5fG80fvV/yVqdpPqJjvcdg/mgYet3LnGYssZd3RJNzVP1rp/PgtedKsqzAKYfmcVakrVWjCI7pDhWDt/sBte3qiY1ZyVZi058KoBl0slawztkpFK88hYZpO43qGStgRdrJTr3cVhJWfV0n+7bIFajnUye5EWHWlnJWtVOtQOgWrITHuqL/r/ssT0la2Vt//OrRIlkQka/5wSjSaet+rgYoAyZF467JWt1OG6vpGKtQslaY5C0Pmz9ziUO07DHXTykn89CFT9HkOdY0o+sFEvFWitHsWQtx8ph6359y3sybOmUd+N3lqv+VX5g6JIr0Ie4aq/fCYlBdYbpbRAHO+BJrbgbh5WUyQSGnNvXDJptEBmVuVbnz9ZUY7DbIPpMA9BJPwkPZSZr1WoRq37VF/a7/U/VkrWS2zrqm4EJkp2s9av+pJH/+J7aBnHFF2uNweK9YUslclaxnx32uIuH9FPo5nWnivIcS/qR1dem+mcmT5Hjn2Pl8Lm+VT2p19w2iHTiUwEsU4VkrX6jvtPFWv0ma40mqSaZCjAGk3PJwrsu8azD0n0bxGq0k8kz6BXitkwAoIissdl80lVyLFx8vJS1zXftVymTVT0n6FVqoYW+GZgk/SVrddoGMbHIZQKLtYpcWB+HxXvDVuRzNWpZ466N722Z4y4e0s9noYqfI8hzDtePqXq6r5GstXIUS9ZyrBw2O8dUT2qsZhtEOvGpAJZJpUSlVqgNQrrAp/vAot1uD6xyOSvxq90e3AlK+vepfmxm1bdis/KAUUmtQCvrooNkLQCKyLy4PsRtEBf+jH77smRB2MiStVx0BSZfviKU/HM+6WStyZvWTvUTne87eb9/v/qZSxy21Bhlql6LqUZ2UaNz+t71tw2i153qGXyyVvp7qR0DmDxFjp36qOFzfat6JGtRhE8FsEw6WWt4hUH9bJ03yJOUrMdvaA6uWKtqF5qKqHpsu5UHjEpqBVq9NuhkLReEAVguc9uqAWzHkx7fPnzOkU6iyteXVW2CLPX7bGi2o2VlOjAhUnMyjQVFKEXmfFLHx0lM1iqySDK1leRKlv5cVe8cOM9WzRI5y9fPXGRqcTOMUlYxahnnPFmF0Wq1VoasYIRO9FHD133nGO/JsOWZ74KNqn+VHxi6KlzUSKYx5dg6L2vw2G8SVdbjB1kAUYX3pFdlJjAMgpUHjEqqWGuqUVayVufjlc80AJ0MP1mr+/g2mSiRYwFHq9VO3m9U6bRZKSh5fieAcZC6cLw4OTH/nE8qWas+icVaBdKyJGstl/5cVa+P7Wsc5Jy+Z7ZBZNJknUOUcd0gqzBastbKMNdqR5F1RQpbh8/1rerJM86DjXwqgGWqkOKU3PIkx0WMrJPnQSZrDXLQU4X3pFfpdJ9qDBK7J2s5wWAwUsVajZIuOlT9bw+AakmNZeu16J6E0kPfkmcxQjJRIsdF10FvCdKL1O8ToX8GJkfq+LsoMajAnE9ykcsEFmtl9RP93HelSL0mVSyITqV9SdYarP6StbzuVE/qvGjhOVw/suYoU/0zk6XosU8fNXyub1XPOF/PZfh8KoBlqpDilFy1n+fCTI5UgF5lPX6QA9H0e1L92MzkysaKDBKtPGBUUivEy7rokCx69ZkGoIPU2GzhWC6Z2thsFd7GL8/kVWob9n4XcIxqNeMmGSkoVRkbA/Qr3Z/UO/570WM7zPmkzpvKWuRSJZK1+pN6TSqZrJVMoFsw7kqMg1wI751kLSZNqvCzrGsG2clairVWgqLHPvPOw9ftNdd/DV8VrrEzPnwqgGVSE2uTkKzV72TW6JK1Rv+e9KrMBIZBsKc3o5JO1irn7zq9VZWLwQAslxqb5UlCiSieWpGcvFq1sDismgs4epV10aQqY2OAfvXTn8xK1sp933FYvDds45WslePvJJms5Zy+V5K1mDSp86KyrhlkbTksWWtlkKxVfa5vVU+exZCwUfWv8gNDl95Pd3gdSXr7ru4TEln36TcmflTJWukVd9U/jKcLRqoxSOweE1uNdjJ50sVa5Ty/bRABKCJ9DtA9CSXr8en7JyavGjku5vd5TjCqCbKsiyb6Z2BS5JlTykoBbrcXnycNevv4KinSP43D4r1hSyZrVbC4Kc88X/r3MWboVT+fhdm55rLjE4xannO4fmQVRivWWhmKHjer2OdOOte3qmfQx2Ymi08FsExyFWRZFQQ5JLfO63cVfSJCPK/sCyyDG4h2Wl0aMR6Tc1Uv1hITy6ikt/MoK1krsXWUzzQAHeRJcs0u1io6iZtK1lr483rvy7LGcKMaQ2e+fhXcogmgF8mUj0Zvxb8rqVirSP/kYs9y043e5xKHLde4K5UU1qGokXz6meNrtW37RvXkSbPsR1Zfq1hrZbANYvV1e829J8NnG0SK8KkAlslz4WTQklvn9bkNYv/JWsPfuqTdbo91555eMVuNVRbdVx5Uo51Mnmar82evrO08JGsBUESe8WaZY+E8C0T66cuquA1imdtIAlRV6niWpxi30+PnBnzeVCVF+qdxmA8attS8ZRX72Fzjrox52Cr+TuOg37lbF7ypmvRWW2Ula6WfR/HiymAbxOpzfat68hTlw0Y+FcAic81WclXEcJO1Ehdm+kjWqtUiVjX6m8xb1ahFLfEUgxqIZk3AjMMex8mUtIoM3K08YFTSyVrlXHTI2loEAJbKtW1VxkXD0rZBXHQxv/PPm2u1u67krmKyVtb51OwGE6jAZEgdz3Jvq7tk3iedrDV509pFFkkq1lqun7nEYcu3XejwF4xOun4vWFdlLhM2Sn0mp0u6ZpDV1aYWoTJZih439U/D5/pW9Yxz+AbD51MBLJJZGNTnFoJF9JeslahabtSjlqq0yqlWqyUvsgxq0JM1ETAOnXvyvazAILHVanf9TJmIYVBaqYsOfR6nNkqnkbgYDMBy6Yn+BUlXGcVGw0zWyvPzqpisNdWoJ4uyJWQAk2I2cTxbeOG4SNJgqlhrqs/FeFWU2sav433HYD5o2PqZSxy2XOOuAtuFko9kLSZNuvBz8MlaFTy0MgDFF2X5YAxbt7l+78nw5RnnwUY+FcAiVbmokVo91my1Y67LmcCgq5aTK/UGVABRlfekV+nXa/SDxDwTdiZiGJRkslZJFx1Sx1GfaQA6yTPRX26yVp5Eid6LtVJj80a9FlNDTAxeapxSPwB6kTqe5d1Wd2kyV6pYq17SIpcqKZasVf2k9WFLJrtXML0y17irj3EQnfU7F2nxG1Uz6OsgWeH/qW2KmSxFj5v6p+Hr9h55T4YvfWw2fme56l/lB4aqKtuFZE5I9JiEVFYyWCpGeFDFR1V5T3pV5WStPO+ZiRgGJblCfMDbIFahUBKA6kkmXQ05WWvxNogZF/O7jNHyJHeNQnKbYkvTgQmROp7lTQxa+vjUIpeyzpuqpEgfNQ6L94ZtnJK1co27JGuVTrIWk2bQ6S21Wi3Z33bblp7JUPS451rK8PW6kI3BGXTqIZPFpwJYJGtF9zA7kswJiS6rzlPfL+vCzLALILJWAI5DJXZyZWMFBol52mACjEFot9vpZK2SLjqkt0H0mQZgudS4aOFYLmsbv6Jju+SFhUa+RIlu/Vl6Acdop0FsUwxMunzJWvnnfFIXg8s6b6qSIn3UOCzeG7ZxSq/MM+7qp2idzvpP1qreZ4mVLc+xpF+p/jY1r8lkKXrc29BsR8tnY6jyzI20296TYUodm43f6cSnAlhkfTN9sj82yVqJ75d1YSa5Gn5AJ+xZv+84dO6pNrba0XVLy0HL855ZNccgZJ2zTtXH81gFwHjLu4VGagHEYJK1ei/Wqm6ylm2KgcmWmsOYKTtZq6Tt46ukWLJW9RfvDVuyIHqMkrVy/50YN/Sk39dNsRZVM+htECPSxVrNpuKPlaCX42YVEy0nWbe+qd1WXDlstkGkiOpf5QeGal1mstbwOpL+krUSVcslXZgZdlpNVdLOetXPRbZBy7cNopMLypcVFV7WNWTJHQAUkTemPbUAouiYKbnSsPHwOUf29j/Z/VlylXllk7WMOYHJkJqTWZTUWK9FKhhr6RxIKp2hXpu8Yq2ZVfnn3cZh8d6wZS1YqlqiRJ5xV5Xn08ZVv/Mh5lOommFstZUs1qrYcZXB6OW4V8VEy0lm95jqGfQWtUwWnwpgkayq92EWBvUT9Z1cxVlg0itLehvEwZywT2qyVsToB4mStRiV7GKtspK1UluQ+kwDsFzeyaTUAoiytkFcnKyVHr93G6Pl2WZxFMZpiyaAXuTpT2q1Wu7FJclkrQncBrFYslb154OGbZyKm/JsjzNVr0WqJtFcVW9sg8ikSaYJl9hHpPrbrLlNJkfqPDVrO+rZjN17KJ9rXNUzjNRDJodPBbBI1kWCYV7Y6KfAJ/U7zJTU/mFvXZL6feq18ZiczNzSciyStZxcUL65VvqzV9bfdbqw1MkZAMutTyVR5UzWKmsbxIXnHP2cE1Q1dj71O9kqApgUeS9O5J1bSV0MzrpIOK6KpD+Ow+K9Ycss8q5YP5vn76RWqw19weik63c+ZNTzmLBUMk241GStzs9lW7WVIdV/rt5kKvkYC5H+/+3deZgcZbnw/7uXWZLMZCGEkI1AIAkhEDwBEuAFokjUiCwiwgFEFmXxSFREA8jrCaKy/BAC/GRRQRbZRFSWHEM4gohIQhIIAgqBkCCQhewJkMxkprveP0KK6Zm+n66qruXpmu/nurjonqmurnTXPOv93E98HMdhjssyhaKjlo+031EOdwWAElrjqz6XlWyMA2HVrIbTM2uFFKwV0tYvXm1VViI05HOSqYG0/6aMZkk3Ell1gKSYM2tFG6xl4xYQAIDk6ZmuSttyYWVu9JJZK5fNqEHMQTNrJb0Nop5Zi8FTAOngdeLY6+ISbaFLPmf/eIhffibXWZnflXEs0bKJY33rMm/tLsaqgiGzFtJGDfwMaYcRERFt/XvBsBAV6aHVn70b69TX2BYgnWZtBW9j/LQb4mP6rGm/oxzuCgAltEmCuCN+q8nGpGbWCunfoGUYizuzVq1EYZsysiXdSGQ/byQllmAtw2Q0nWYAQGdeMl2Ve17p9X7fr+skZbCFEvZug6gEu1E3A0gJdRFg5211DYtLOtKKx7C2j7eJv2CtZDNF2sg0TmZbH9jr1mX6dqF2/XtqRbXjkEmPYwKdxdHnySv1rWXFKiKiJRIgs5YdvCZDoN0QH9N3Qvsd5aSvVwugKmpWqpgDgzKZjNqpqNQA0RqQYQU36Zm1olkNb8t3EpQpYCTpRiKZtZCUOIK16nOGLSC4rwEAnegZHrxtg+g/s5a3NrvXyXyv5w9zlXkQ6qQrA9oAUkJfQOc1GLe0/NYyd+RqINO4X34mcOpSmFmsWqbPz7YMlp7bXQRrharasdukdwgAOvO69XA1tHFKMmt1D1q7zhSsZVuAdJp5HeNnLiA+ps+6VhJwIF7cFQBK2JTFybSFl4nXgcGgyKzlj2klT9KDS17ev73oSDsdDIRM27dcRNTtnvyyOVASAGAfzxke1AUVIW2D2GWSUtt2scICDmszayl9CdqbAFLCe2Ytb9vqan2nsBa52MTrOE9DPiuZFAarVYvMWqiEzFpIGzWzVojzBto4pWlsE+mh1Z/Nhm0QbQuQTjOv7QGCjeNj+k5qJQEH4sVdAaCE10mTOAQdkIg6/a8+YRRRsJaWFaBGKnbTdSbdSPT6/rYN6qH2xZNZi2AtAIB3XtucWmYqv+06rxMLYfcJTMHMcSCzFoC00xfQBcsYpPWdwlrkYhOv4zy1sngvbsbxJ8vqWb3d1TkDndLuYiI8kGrHQhhLgW3imDfQM2sRrNUdaPVnz/qcem9QVsbHc7CWZe2gNDNvg0gbHl1xVwAosdXjdiRxCJrqW03/G9LETNwTLPpEVm3sb2ze0tL+bRD9HAd4Zc6sFe2WrSLc0wCArry2OdV2nY+2sOM4nrfsCD3bbuKZtcIJdgMAW1W7za3nzFop3AbQe2at2hgPipvp80t6/KmzaoPWWVQYTLVBbkx2wzZ6lr7w6gktIKe9QLBWd6C263LZwH11hMfrZ91KuyE25sxatOHRFcFaAErombXir0TUAYmKq+j1BmQY4t66xKZsZ0HZ2nD3niaWxizCZVp9FlKsVoXMWkwIAwBKeQ6eUoKB/bSFTcd2fj99Mr/CNojKeySdWcvWdjEAhEWvTzpnDPJWHha1YK0UbgPodeytlsaD4pTPZkRLuGZbPVtt0DpBQ8FUO3a7tcBYCuwSx7yBFqxVdAjW6g5M/Wq26k2e1zF+2g3xMd3/ZMdFOdwVAEroqzGSyKwVbNV51BMz2nmiCn7wOoBjM/0zI7MWuidTsFZ4mbX0gX7uaQBAZ163DQxj0tDPSsPgmbWiXcARVNDswQBQK7xOHHsZ83EcR82s1Z23Qayl8aA4ZTKZmshgWSjq97XnDKNkyPDNcZzqt0FkshuWiWMuR6tvTbsGID20cq8+lzPUUfbUuWnneX6LdkNsTN8JwVooh7sCQAmbsjipq/aDbnkSUnaw+lz580QV/OA1NbrNtEmxpANGvGfWooOBcLUX9XtPW7Hml6ncZkIYANCZOtCfC3/S0NQG9D6ZX2FrdHUBR7Jp58msBSDNTEEonccwvJSHpnngsPpNNvE6zlNL40FxC5qlP05+JvHIrBWe9qIj1SYCYrIbtkkys5ZpISrSw5QYQe2rU0fFxvP8VpXbAMM77Tupy2VS2X9B9ejZASihNr4SGAjSAnyCTsyENZgVd5Yor1sI2EybFEs6CIptEJEUc2atcBrt+WxGtJ1BbBqoBgDYQWuXdW7H6QOy3tt1ftLCB94aXV3AkewwSNBtHQGgFvgLxq08tmJa5JLPpW+yw7SNX0dJ12U2q4UMlqY6v2uGUWXBKFlLfAvjHiAAAbZR5w1CXKCi7QDQXiBYqzsw9avVvjqBrbEhs5Z9vC6EBLbjzgBQQt0uJBWZtcL5N8SdJUqdOKuhwTl7M2t53NPbokE9pIMpVXhYKyy2bQHBhDAAoLJi0ZE2ZbC9czsujAFZ82S+t20QAy/gSHwbRG3SlfYmgNrnJ2OQl2Bc0yKXXEjbx9skk8l4Gn+rpcV7cUtbZi01yJugId/CuAdor8EmxaITS59Hq24LhoBqpEerITEC2R+T53l+i+8kNl4XQgLbpa9XC6Aqemat+CsSPbOWuQHSfTJr1U4RHvdn5pXnlQcWDeohHYoxBGuJ2BsoCQCwi2niqXM7LowBWVN73vMkZaUFHOoAmaWZtRg8BZAC/jIGVR7zMQZraWmEa5yX8Te2QdTVwoIlUxvGawY6gob8C+MesOk+Avz04aqhZdYqVLuvKGqCltyhIZ8js5YFvM6v8Z3Eh8xa8Is7A0AJbZIgkcxa2hYrFTNr6Q3IMKhBZBHt+6z9e2tpcE7/zJLOrMU2iEiGMbNWiJMO+hak3NMAgI+Z2mRdMmsFXFDh9f26TlIG205b7dcknlnLzkUMABAGP0EoXoJxzZm10hms5S2zVu2MB8WtPuBYYpzMQY1k1opKGJ8ZnztsYtxaPsQ+j1bfmsY2kR5q4IkxsxaBrXHxPL9F/RUb7TtJeuEg7MWdAaCEWpEkMBAUNHV51MFNWvBDVNHpadgGUd3SspBsw91rI5UOBsKmTTpkMyLZGDJr0UEDAHTUamiTNXbOrKW263xsg2g4NqzMWmrG4IRTz9fC9kwAEJS/jEHK2EqHc5gmgvO5dAZreRnrqaXFe3GrhaBo89+J/wx08CaMcVsyk8AmpnKgcx+uGnllnLJQIFirOzDNF9ZCgHTaeU9GQLshLtpcYtILB2Ev7gwAJWzK4hR0gCXqbQPjDn4wrV6oFbYGjHgdZGEwBmHTJh201OJBqVuQck8DADowZ9bKdXoewjaIyrHZTNfJAHX7n4rZdm3NrBUsUxgA1IKwt7kls1Z5tbR4L25Bg7zjZMyG4/HvhHEq/8ishbQx9Yc69+GqoS0qJbNW92AK1qqFAOm087roi8Vh8dEXDtJ+R3ncGQBK6Fmc4l+Brq7aD5hZK6zBrLiDH/R/T7JZAfxQV8wmPLjkNWMWgzEIW1HLrBVyy0ydECZbHACgA1ObrHPbN4wss1p21YZ8TjKdtgNW296BM2slHaxFZi0A6WUqy7xmDNrqNVgrxO3jbeJlrKeWxoPiVgv1rDHAwut20IxT+RZGdn8WvsEmxix9MWTWKjoEa3UHWw27vtRCgHTaeV30xXcSH1sXDsJe3BkASkSdlcoPbQWIqQHiOI46MRPaNoiGgR8ngk6KTd9JUOokG5m10E3FlVmLVbgAAC9MbbKu21ZFl1mrXL3VoAxoaYPGH7+HPYtQOmJAG0Ca+ckY5GV7t+6YWYttEKtTCxkstb+Tulymy32tjkHSp/ctnMxa9txHgDlAOrx6QqtvyazVPZgSCdRCgHTakVnLPurCQRZbQEHPDkAJq7ZBDLCK3riiJKTK0NTZiWKwxKbvJCgtajzpwSWvAzUMxiBshWL5ey/sCYcwJtQBAOlnapNFsR2PPnhVJlhLyeQVNLNW0m1orW5uLzrGoAQAqAXaRFA+2zUIpV7LwN3hHKaJ4HwuncFabINYnVqYONaupdzYmRrkTZ/etzCyYiU9jgl05CdAuhpaZq1Cgb5L2lVKjFALAdJp53XRF99JfNQAR7ZBhII7A0AJm7I4aQE+pgaIcQuX0DJr6UFfUayIt+k7CUoPvEu2kUhmLSRFz6wVT7AW9zQAoCNTYHrnNnkYW+z6yaylBv0b2t2FoiNtyuRB0m1o08SJTRPJABCEnn2hTDCuh0yD2iIXETJroTwv91XStLGwcgHqWruLPr1/4WTW4nOHPYxbqoa43VZO2QWAzFrp11ZwRNtIpiGfrYkA6bTznFmLdkNs/ATlAyIEawHoRB0wqJXMWj62cAnKNCgWRadd+060Vag2Ure0THiQw/Oe3gzGIGRa5gwyawEAkqC1r3PZjOQ7DSiFsY2fnz5HkD6BceIi8cxapoUfrHYFUNv08QsfGYO8ZtYKeQt5W3gZu2IbFV0tbDesBq37yqxFm8GvMNpZNt1HgHZPl+vDVUM7lSmgGulgKjfrDcFalJXxYX7LPn6C8gERgrUAdGLagzpuQVbRV2pAhsGWbRCTzgrghzbJlnREv+dtEOlgIGRxBWvpA9UM7AIAPuYnk2sY2/jp23x37XPo2Xb1uszUX0i6DU1mLQBpptcn5TIGVR7zMdUruUxaM2tVHn9LOvDYZvp9ZU8fWN0OuszYGRPh4QmjnWXTfQRo49ph93e0zFrsgph+5n51jnFnCzC/ZR8/QfmACMFaADpRUzQmklnL/57XlRqQYTBn1gq/IWrTdxKUOsmWcEQ/2yAiKdoK8fAzaylbJtBBAwB0oAdPec/wIOK9fvG1TZYW9B9wAUfSwVqm92cAFUCtC6M+6ViGG4O1cukM1vIy1pN0XWaz2sispWSg85FZiz69f2HcAzbdR4A2Xh32nEFeGasks1b6mco8U2Yt6qj4eJ7f4juJTauPoHxAhGAtAJ3YlMWpIVBmrei3PDEFfZFZqzx1+5payaxFenmETJt00AZAgtKzkdBBAwB8bGvBx7aEIQRr+VmMoG6nHbBPkPTWUQRrAUgzf5kay5fHbQVHih/1l8zbIKYzWMvLWE8tLd6LWy0sWCKzVjJCyaxVKIrjkE4IdvCztXw1tIWl7aTWSr1KGatrIUA67bxn1mJ+Ky5k1oJf3BkAStiUxUkN8DE09uLY8sScWSv8hqhN30lQematZBuJZNZCUmLLrBUgGwkAoPtRB5N8Bmt5HQD0lVkrwGrdOBZwBGUKFmMAFUCt08ox35kaP+qDGzNrpTRYi8xa1amFiWM/k3hqZi2ChnwL4x5wnG0BpYAN4poz0DNr8beQdpUza9kfIJ12XuetbGoHpZ2foHxAhGAtAJ3oKzLiX4Ee9sRMWINZxmwCIQf1FIuOXrknnBXAD21Ly6SDoLwGiyW9XSPSp6Dc++Fvg1h5axEAAPy0N83BRtVl1ir7fpYu4AjKNEDHoDaAWucvs1blTIOmrB25TDqDtbzUU9oYC4KNJcYt7nYXtgnrHkh6LBPYTl8AE24doWbWIlgr9Sr1q8n+mDyvY/w2tYPSTptzrKX5XMSLYC0ArvZCUbQ2dhJRv/pqOL0BojU6shmRfEhpJvPZjGjxFGEH9ZgGAGopElttuCccBOV55QEDMQiZNueQy4b7d21ahQsAwHZamyzo5HrF9/OxZYeWobVQdKRdqc9M/YWkB8hMqe8Z1AZQ6/xMHDcaxjS2l+NFJXNQJiOSTWlmLS/1FNuo6GphwZL6d+JjG0QR+vV+hXUPJL1LALCdnwDpamjBWlodjfSo1K9Wx53p18bG62fNWEN89KB82u8ojzsDgMuY1jSBgSBtgKq1XU/1HUdmsEwmEyiQLAjbvpOgbFxlUSw6nlOXJx1UhvQpFMvfU1pq8aDUcpR7GgDQQVjbVkWyDaIhe4g2CGbzNojZbEbqcuXrewZQAdQ6rRwrW5/kDBmDPuqvaFk7wu432cTTNog1tHgvbvWGsURbaME+5cb5jEHy9Ot9CesesOleQvfmp86thlbnmrJfIh1M5V1dLmOYvyOoNS7eF8xRd8XFz2JIQIRgLQAdGNOaWpRZy3H0Abu49mqPaz9u0/lMq1Bto6ehT67h7mcFIqsVETatDAt7G0QyawEAvPC3bZUheKrKbRDLT+b73zZQ+3k+mwm9rg1C+zexAhlArfNVn5i2hf2ov6ItcrGhLI+Kp20Qa2jxXtxqehvEMgHqxnYX/XpfQtsG0aJ7Cd2bnwUw1dB2ASiwDWLqmdp1poQGlJPx8T4GQwBdXLT2WdILB2Ev7gwALtuyOAXZYiWuToqeWSvchqhpFYJpFapt4vq8/PCzApEU5whbQVl9Fn5mLTu3IAUA2EVfld21vWnOrBX+NoimyXy/fQJbBse0bGGsQAZQ63xlajRtC7s9s5bab7KjPI8CmbWqY+P4U2daf7zc34Sx3cVYlS+hbYNo0b2E7k2vc8OdM9Cq63YloBrpUaldZ+NuKt0NmbXs42e8CxAhWAtAB8Y9qA3bj0TFNCChRYz7GRisRlwr9UyNqFoanFMzkRX0LS2j5meQhsYswlZQ7vtsTJm1mAwGAHTkZ8FDLptRg4vjzqylTXb6yeySBDJrAUgrvT4pkzHIQ2atotZvSm9iLWMmpe1qafFe3Gohs1armlnL5zaIFv2bagGZtZA2cW21pWbWIrFW6lVq12njzu1Fh8xrMfEauN3antw8XHejBuVbMh4F+3BnAHCZOpvJZNbSB5+0QIO4JmbiCoCw7TsJyrSlZVtCPTs/g1oMxCBsWoc1/Mxa8WzZCgCobX4zUVWbtSK8yfzybe+4FnAEpf2bmHQFUOv81CfmYNxt5bi2fXy+hsZD/CKzVnVqYcFSWJm16Nf7E1Y7y6Z7Cd1bXFttaWOV2lbFSI9Ki7pMc27UUfHwsyVyUvNw3Y263XXIWQ+RHvTsALhsy+IUpLHnZ+KnGtr5wt8G0a7vJCjjd+mjQRkmP98VAzEImzbpkItrG0Q6zACADvwueKg2a4Wf9zO141t8Z9ayY3CsFrJ+AEAQfsr3fC6r9n+2jxNoi1zC7jfZxMtiQ1syRdpIq+vbCo4ULcnyoU7ikVkrUmTWQtrEl1mrfJ2rbVWM9KjUrguSbAHh0sqBssfyncQirrIZ6cGdAcBl6uQnkcXJtApEu1a/WQGCqjabgFdpz6wl4j1Va9j8DK4kFVCG9Iors5b2t8fgIgCgI23QThtMqjZrhZ82e5Cg/7j6BEHVQtYPAAjCz7a6Ivq4xvZJDm0iOOx+k0281FW21Gc2MmaismRsRxsHayjz95DPZdVtP+nX+xNeZi0+d9hBz94ScmatnJZZi2CttKvUryb7Y/K0rZXL4TuJR1xZD5Ee3BkAXKbJgSSifo2rx5ToZL8Dg0HFla1G+05y2UxNpf03r7JIKrOW98kwPysUAC+0SQcyawEAkuA3O221WWb9BIeZt8nS+gT+gs/ipn5+tDkB1Dg1CEXL1FhhW1htIjibSW+wlrfMWnZkirRRkLHEuKntrjq/7S6CvP0I6/Pic4ct9Do33DpCq3MJ1kq/Sv1qsj8my3EcXwFYfCfRay8U1bKR9js0doxUArCCVrHX57OSSWAgzLwarnxD0fRvCFNcW5f43ZLGVjbuX05mLSSp6MQTrKVm1ioUxVGuAQDQ/diyDWJ9mcGrbDYjdcpqbq2NZnsbWgtAo80JoNbp27tpQShaebhtzKeg9Fm0LB9p4GUih5X5ukYlAFBEpFUZS4yb73aX8m8iQ4Y/YX1eTHbDFrFl1lLGKrU6Gumh11e5j/5PsFaS/I4f0G6Inuk7of0ODXcGAJe6siuhDE7hrqIP/BqvYQAAQd1JREFUN2pZz1YT7sCP7Vu4eBVkS8uo+XlfW1ZfIj3ai+XvqXw27MBS+7LaAQDso7VhtTZcVNsg6sFhWiaq8u9nexu6UiYZAKhVWt9ZG99R65Pt2yAqK9PDXuRiEy91lS3Bxzaqzxn6wJaM7fhud2nbhdJu8IVtEJE2ap0bch2h1bnargFIj0r9avO4sx0B0mnmtz6i/oqeqa1J+x0a7gwALjVS3rAqLUqZTEYfuEt4Fb3WECWzVnlk1gJKaelw49oGUYT7GgDwMXsya/mbzPefWcuOtPNqZi0GTwHUOD2zlr9g3O3nKSjn07J8pEGl8Z5MJt3//mqZxjBt6QP7zYZDZq1whPV58bnDFrFl1lKyWbINYvpVGicw7oxDWRk5v58x30n0yKyFILgzALjUSPmEMmuJGDJYqZm14umk6NkEwm3w+F1tZytzZq1kVln4+a4KRUfaLRnUQzpoq8/CHnQ3pqO2ZFUxACB5fjNRVdsW9p9ZK5w+gS1taG07MFYfA6h16hiGlllLyxhUMbOWHeV5FCqNXzXks5LJEKylCZKlP25+s+HombVoN/hBZi2kTVzzBlmlztHqaKRHpXvMxt1UuhP/mbVoN0SNzFoIgjsDgEvdQlCZTIhDpVWWnakTPyFnB6s2m4BX+kSWHVkBvDINliUV0e+3cUoHA2EqOOUHNLKhB2uRjhoAUJnfTFTVZpnVM/pq7+cvOCyuBRxB+f33AECt8JuxXc0Y9NGYT1HpNyW4pjBylcZ7am08KG61nVnLX7uLdoM/YX1eZCaBLbSggLDribwSIK3V0UiPSpm1ctmM1CmZ16ijotfaxvyWbUzzLbThoUlx1xaAX+p2JFZm1ipf6W31uYozKD2bQLjBDzZ+J0EYt7SsgW0QgxwPmGipwsPOrEU6agCAF3Fm1ioWHXWSUs28oi6UKN/2tj07rW3tYgAIi56xvfzkhJ5Za1s53h0za1Wqq2ypy2xlzqxlx4IlNbOWz3YQ7QZ/whqzZeEbbKH2qUKuJ3LKWCW7UKSfl3ECre5i3Dl6foPQ+U6iZ2qb0YaHhjsDgCuurFR+qBmsfGfWCjdqOfHMWgl+J0HZlkHAf5pYGrMIjz7pEOM2iNzTAICPVFox6/XnXuoW04CimnnFZ0YJv5nC4hZXXwIA4ua7PtG2hf2orijEtH28TbxsgwhdPpdV+9XWZ9ZS20F2jafVqrDaWbTXYAs9s1a49YRW52oLUZEeXvrValuOwNbI+d3eme8keqa2GW14aLgzALj8DqrFQV09pjRE4spEFVcKctu3cPFD/8ySaSSSWQtJimvSgcxaAAAv1O3QfW/HU7ldZ1xp6DuzVm1ug0iGDABp5XfBmZ5Nfdt54lrkYpNKi/NYlV9ZpfsqSe2FohrgoLa7lIlw+vTeOY4TWjuL9hps4XdL1aByyjZ3Wh2N9PDSryazVnL8BqFTf0XPdN/X2m5JiA93BgCXjduF+F09FtfETFwTLPp3YkdWAD9sWwnoN0iMlQcIkzagkSWzFgAgAXFug2hqUzWGlFHC9mAtNdjNku2ZACAIUxBK0O3dtPPlMukN1qo0kWNLlkib2RwUHWR7HHW7UMapPAszqxqfO2yh9R1C3wZRqXPJrJV+XuYLtSBzG+rctPOfWYvvJGrq30wuG/q8D9LDjpFKAFawcbsQv6vo1cxaIXdS4tq6xMZsZ0HZtt2L3/elMYswFZ14Mmvlc1nRTskKJwDAdmFtg+ilbjGvNCzf71D7BMrE21Y1U5gdbWi/W70DQC0wb3OrZWo01ycFrd+kZPlIA9M2fiJk1vJCr2eTD7IxtYP07ULtGk+rRWF+VnzusIWeWSumbRCVOhrp4WW+kMxayfHbruE7iV5c89NIF+4OAC51RX2C6Rn9brGib+ESV2atcAd+/GY5sJltKxv9vi/BWgiTvp1H+H/btm1BCgCwS6HoqPWS1n6rJmOq6Rj/22RpfQK729A2b88EAEGZyjBtXEmvT7aV72pmrZSvTDeNw9kSeGwzdfzJgno2SGatBjWzVvL/nloR5mfF5w4bFIqOtBX89eGC0upcxxEpkl0r1bz0q/XMWow7R43MWvaxPcs77MTdAcClVe5agysOflftq5WhsoozKC34IezodPU7qcHK3bbMWv6DtehgIDyFYvn7L+zMWiKkowYAmAXK8KC26yq3l4K9nxZ47C/bri1bR5FZC0AamTNr+Svft5fj7cpEdBT9JpuYxuFqcTwobup9ZUE9G2ZmLfr03pFZC2kTpCwJypTNUlv0g3TwkoFb7atbECCddsxv2YdgLQTB3QHApQ1aJJlZy282JjXNZMj/hriyRMWVzjgOcWUj88rvd8VgDMKkTTpEsUKcdNQAABNTW0xrv1XTFg6SUcLv1ui2Z9ayrV0MAGEIklmrUn2iLXIhsxZMtM/PhonjQO0utU9Pu8ErMmshbYxby4eeWUs/n5YBE+ngpV+t1lEWBEinnd+xfeYCosc2iAiCuwOAS91CMBWZtcL9N8SVJUrb2sWWrAB+xJWNzCu/k2EMxiBMcW7nQTpqAICJeVV2+fZbNe0648SC722y/GbWsmMIxLZ2MQCEYWtB71/43ebWzazVTbdBNGfWqr3xoLhpn58NE8fG7aC1dpeyWwDjVN6RWQtpYxrTC7ueyGVMmbX4e0gzT5m1yP6YGOa37KPOsdN+h4EdI5UArKBnpUquIvG76ry7ZNaqxUjsuD4zr1h5gCQVHDJrAQDsYJ40jCKzVvl2fC6bkXzAzCte38OWNrRt7WIACEOLIWtRgzKupGZA+qgcL6r9JjvK86iYxrBsqctspmfWSn7BUqB2F336qoW5WI2Fb7BBkLIkKNNYJbFa6ealX00dlRx2jrEPmbUQBHcHAFdcWan8UPe89jkxo61CC0pf3R9uh11L0W5LVgA/4vrMvPK/pzeNWYRHy6yVjyKzls9yFADQvZhXZfvNdFW5XRekfavXZeXfT+3XWLKaUf38LNieCQCCMk4ca5m1KmRj0LaPj6LfZBNTfVWL40FxszkTlXEbxJAyjKIrtkFE2sQZrJXPkVmru9L77h/Xs3qdS2Br1PzPb/GdRE0fi6L9Dh13BwCXjRWJFnFcLkK5WHSkTRnICzuzljZw1lZwpBjiXu1aZq1arNzj2jrSKzJrIUnapEMkmbUY2AUAGATbjid4uy5I5lg/fQLTz21pQ6v/Hgu2ZwKAoIJtc2veFjbO7eNtYlo0ycr8ymwObjLtCJBV7utq2l3Yhm0QkTZBtrIPylTnavU00qFVm5vqUC/pWVIpK6PG/JZ9yKyFILg7ALi0BlSSFYmfARbT5EbY2cFMn0mYkyy2b+Hih20BI/739GblAcIT56SDzQPVAIDkmeoDrf2mbZNezTaI5sxafrdBtK9f05E2gVIoOtJOwBaAGqWV73W5jBqEUmmcoF3rN2XSHaxlWnBoS+CxzWwbf/JyDcag9QrbhaKycLdBTP4+AoxZ+sLeBtFQ52r1NGqf4zj6IqgO9VKlLKmIDjvH2CfIeBfA3QHApa9AT267kEqrLDuKM/2v6XxhNnps/E6C8vNdxoGVB0hSwYlxG0QlHTX3NABApEImFJ/bIHrKrBWgfevn/doLRTUo2pYBMtN1kF0LQK2KonzX+k05w5ZMaaD14URqczwobrZldvdyDcagdfr0VSOzFtImSB8uKDJrdU9eEyPYXOemHfNb9knTfC7iY8dIJQAr2JjFSV8N1/VaTStK4g3Win61li1ZAfywbWUjKw+QJD2zVvh/26zCBQCYaG2cfDajDsxX064L0r7VM2t1rctMg8q2tKFN19HaRpsTQG0KlDGoQvleULaPj2KRi01MmbVsqcts5qfdELdwM2vRZvAqzM/KhvsICNKHCypvCJAmWCu9jBm4O2Tatm3Opzvxv3MM30nU0jSfi/hwdwS0fv16ueKKK+Sggw6Sfv36SWNjo4waNUouuOACWbFiRdKXBwSiTQwkuQLdz5YnpomNsCOXTecLM0Ldxu8kKNtWWfgP1mIwBuFpL+qDKmEjHTUAwCRQhgfld1628Qv2ft63XTS1LW1ZzUhmLQBpFGb5vv1c6jaIKQ/W0vpwIrU5HhQ32zK7e7kGc2Yt+vTVCjdYi88dydPuwyjqCFOdyzaI6WXsV5dk1lL66m3MpUSN+S37xFk2Iz24OwL485//LKNHj5Yf/OAHMnfuXNmwYYO0trbKG2+8Iddee62MGTNGZs6cmfRlAr5pEwNJViRaxHG5xmKcq+iNq+HDTK2t/JtqMRLbT5a0OJAmFknSVohHMenQoKzC5Z4GAIgEy65r+l2lYKMwM6/43RrdljY0mbUApFG4mbU+2gYxxkUuNtH6cCL21GU2sznLR6B2F336qhGshbTR/v6jqCPyhl0AyKyVXubMWh62QWQRUuSY37JPnGUz0oO7w6dnnnlGjjrqKFm9erV6zMaNG+W4446Txx9/PMYrA6qnRbunI7NWjNsghjjBon8ndmQF8MNPRoQ4kCYWSYpzhTircAEAJnomV729afpdpbZwkD6HrX2CoIyfH6tdAdQorfwKUr63F51t2RqVflM27cFaxsxatTceFLda3AbR2O6q08bTkv/31IowM7xsbS+K4xCggmTpdW74dUQuY8qsxdhiWpnKzdLMWkqdyyKkyDG/ZZ84y2akhx0jlTXizTfflKOPPlpaWlrcnx133HEyd+5cWbduncybN08+//nPi4hIW1ubnHrqqfLee+8ldbmAbzZmcfKzGs6WzFphrhqw8TsJyk9GhDho76ut0GXlAcJUVAb2ogjW0lfhMrALAAjW3qwqs5b6fvrglb9su4ZBZUva0HFl6QWAOAVZSW6sT9qLar8p7Zm1tD6cSG2OB8XN5kxUgf5OlH9PW8GRIlltPNHap6ayxPQ7MsYgaXFmb8nl9L8FYrXSyzjX1qFeUvvqlJORY37LPkEyDQPcHT5cdNFFsn79evf5xRdfLL///e9l4sSJ0q9fPznggAPk0UcflSOPPFJERFatWiWXXnppQlcL+OM4TqDVXVHTszF1nYQxRfubBrqCMGfWCicAolh0pE3ZKs2WiSY//GREiIP2vr171Pk6HghCWyEexaSDvgqXexoAEG6mq23nq5RZy//W6376BC3GzFp2rGY0fn7UzwBqVKCMQcbysCDt6vbxtTcm4ofWhxOpzfGguNmcXTpQBjpDpjUmw73R2p/aGGCl39lwL6F70+vcKLZBJLNWd2TMWN2hnWJzNsu0Y37LPnGWzUgP7g6PFixYIA8++KD7/JOf/KRcfvnlXY7LZrNyyy23SD6fFxGRW2+9lexaqAntRUe0DM6JboOoDEiUX0WvR5LnQw7WyueyagacsAZKTOepxcpd3b/cssxazY15X8cDQRTUSYcIgrUs+9sDANhFa3Mat2AyThqaB2XV9wswSVl0RNo7nc/YhjZcd5xM/1bqZwC1Siu/ggTjbj9fIcZFLjYJGjCNbbT7yoY6NtjfCUHe1dLah9oYYKXf2XAvoXtTy5II+jumsUqtnkbt8zo3ZXOdm3bMb9knzrIZ6cHd4dHPf/7zkudXX321euzQoUPli1/8ooiItLe3y3333RfptQFhMHXuk0zR2KAEWbW2F8XpFF2mRftHdf1atq6w9uM2nacW02bqW1rGv8qiUHTUzEZaY5bVIAiTdv/FuQ0ig7oAABFDG9qw2KEhp0+umzJbGd8vwPY/Il3rM2MbOuQFHEFlMhlD/UybE0Bt0sovU/leKQglzn6TTdgGsTr6+FPyfeAg2+NUykCHyrT2YdBgLRvuJXRvap0bQX8nlzFl1iJYK620cjOTKQ2at7nOTTvtM2Z+KzlqO8+SsSjYibvDo5kzZ7qPx40bJ/vvv7/x+LPPPtt9PHv27MiuCwiLKao60W0QDRHHnbcIDLJKvxpq1q+QMmu1GrIS2LKFix/69jXxN9xN93vvRtLEInoFJZVhJJm1fGQoBAB0P3ob2rAFUxXb8YT+fp3qM+38dbmMZC2a3CfzJYC0iSJjUDHGfpNNjNkta3A8KG4217H634lp60tzBjpUpmV+bW7Qtzo0/Y7PHUkLUpYElc1mRIvXKhKslVpaudmQz0qmww1hc52bdtpnzPxWcrZq210btjgHCNbyYMmSJbJ27Vr3+ZFHHlnxNRMnTnQrrDlz5kR2bUBYTFHVSa7aqzes2u98zUFWcVYj6tXwxn3Ba3Alpb5/efyNRNN3pK88oDGL8OjbeYT/t03mDgCASZAMD8ZMVxUza+mDvoHer0tmrfhWmVeDFcgA0kavT/RxHVNd09pekHZl+/i0b4NIZq3q2JTZves1BGh3sQ1i1cLPrJX8vYTuLUhZUg2t3iWzVnp5zcBNvzY5Wl3E/FZytM9Y20EKECFYy5NFixaVPD/00EMrvqa5uVlGjBghIiIbN26UZcuWRXJtQFhsDQwyrSb0uuVJVKsOtWsLbRtEY7az2iu+TassOm9pGTXTZ9vMygNEzHEcNVgrmsxa9mS1AwDYJ0jwVDabkbpc+Tqr0uSVOnhlyrxiWIXYdQGHcn7LVjLatJABAMIQqHw3jNe0thdj7TfZxFRn1eJ4UNxsyuze9Rr8t7uMGehCGoNMO32rKENmLcPv+NyRtCB1bjW0elerp1H7vPartTq3UHSkPaQdaFCe37otiXm47kb/u6H9Dh13hwedA6323ntvT6/bfffd3ceLFy8O9ZqAsJm2K0k2s1b1W57EnVkrrG0QTalia3Elpemaw/rMvDJ9ttrKA1L3IiymgYy8MvFdDbWs4p4GAEjwNnTQ+kX7fdBMXl36BNr5LVvJqP17qZ8B1KpA5btpnKC9KO3F8ufszpm1CNaqzOY6NvS/EybCPdE+9571OTUIxZRZi88dSYs/s1b585JZK7289qupo5KjfUfUX8mplfEo2IW7w4P169e7jxsbG2Xo0KGeXtevXz/3MZm1YLt0Z9aK5vrVlXqhZdbSsxJElS0sSpVWzMbJvA2illmLFOcIh2kgI5uJIrMWmTsAALqg2WmDZm4MlFHCT5+gRlYy6lk/aHMCqE1ByvdcNqMGXpkya2VTHqxlqrNqcTwobto9117Us1zHRc+GY9gu1Lj9NO0GL0zlk/b5NjXkRRuiIbMWkqbf09HUEVq1W1CCqlH71HusUxuF7I/JCZI1kvmAaHn9uwE60sMr4WptbXUf77zzzpLxOJHaMVhrxYoVoV8XatMP/viyPPj8u0lfRhem9JdJDgSZ3vszM/5a8veoDbhEFqylVLBXPvaaXP34orK/88P0ndRiZi1Tg2T/H/9ZJMaxVtNn21tZebBk9Ycy6v/OiuqS0J0YxoajWCGulaPrPtzKPQ0AkDZlZWWlwSStjX3+b1+UC373D/V12kpDU7vf1J7/4k1/T7RPEJT2+f7kf16VK2a9FvPVAED11PqkUvBvPivtW7tObHztjvn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",
      "text/plain": [
       "<Figure size 2400x1200 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "result.plot_info(dpi=300)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Customer</th>\n",
       "      <th>wait_time</th>\n",
       "      <th>is_reneged</th>\n",
       "      <th>is_VIP</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>1.286044</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>5</td>\n",
       "      <td>2.983966</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>3</td>\n",
       "      <td>5.548198</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>55</th>\n",
       "      <td>55</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>56</th>\n",
       "      <td>56</td>\n",
       "      <td>0.257455</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>57</th>\n",
       "      <td>57</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>58</th>\n",
       "      <td>58</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>59</th>\n",
       "      <td>59</td>\n",
       "      <td>5.332226</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>60 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   Customer  wait_time  is_reneged  is_VIP\n",
       "0         0   0.000000       False   False\n",
       "1         1   0.000000       False   False\n",
       "2         2   1.286044        True   False\n",
       "3         5   2.983966        True   False\n",
       "4         3   5.548198        True   False\n",
       "..      ...        ...         ...     ...\n",
       "55       55   0.000000       False   False\n",
       "56       56   0.257455       False   False\n",
       "57       57   0.000000       False   False\n",
       "58       58   0.000000       False   False\n",
       "59       59   5.332226        True   False\n",
       "\n",
       "[60 rows x 4 columns]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "unreneged_custumor = result.wait_time_df[\n",
    "    result.wait_time_df['is_reneged'] == False\n",
    "] # 提取没有中途跑路的顾客\n",
    "all_customer = result.wait_time_df\n",
    "all_customer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 0.98, '未退出客户的等待时间分布')"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1920x1440 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(dpi=300)\n",
    "sns.histplot(\n",
    "    unreneged_custumor['wait_time'], \n",
    "    bins=10,\n",
    "    kde=True,\n",
    "    ax=ax, \n",
    "    label='waiting time (unreneged)' )\n",
    "ax.legend()\n",
    "ax.set_xlabel('等待时间')\n",
    "ax.set_ylabel('客户数量')\n",
    "ax.set_xlim(0, MAX_PATIENCE)\n",
    "fig.suptitle('未退出客户的等待时间分布')"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.10"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
